Intermodal Passenger Flows on London’s Public Transport Network
Automated Inference of Full Passenger Journeys Using Fare-Transaction and Vehicle-Location Data
by
Jason B. Gordon
B.A., University of California, Berkeley
Submitted to the Department of Civil and Environmental Engineering
and the Department of Urban Studies and Planning
in partial fulfillment of the requirements for the degrees of
Master of Science in Transportation
and
Master in City Planning
at the
Massachusetts Institute of Technology
September 2012
© 2012 Massachusetts Institute of Technology. All rights reserved.
Signature of Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Department of Civil and Environmental Engineering
Department of Urban Studies and Planning
August 10, 2012
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Nigel H.M. Wilson
Professor of Civil and Environmental Engineering
Thesis Supervisor
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Harilaos Koutsopoulos
Professor of Transport Science, Royal Institute of Technology
Thesis Supervisor
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
John P. Attanucci
Lecturer & Research Associate, Department of Civil and Environmental Engineering
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alan Berger
Professor of Urban Studies and Planning
Chair, Master in City Planning Committee
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Heidi M. Nepf
Professor of Civil and Environmental Engineering
Chair, Departmental Committee for Graduate Students
Intermodal Passenger Flows on London’s Public Transport Network
Automated Inference of Full Passenger Journeys Using Fare-Transaction and Vehicle-Location Data
by
Jason B. Gordon
Submitted to the Department of Civil and Environmental Engineering
and the Department of Urban Studies and Planning
on August 10, 2012
in partial fulfillment of the requirements for the degrees of
Master of Science in Transportation
and
Master in City Planning
Abstract
Urban public transport providers have historically planned and managed their
networks and services with limited knowledge of their customers’ travel patterns. While ticket gates and bus fareboxes yield counts of passenger activity in
specific stations and vehicles, the relationships between these transactions—the
origins, interchanges, and destinations of individual passengers—have typically
been acquired only through costly and therefore small and infrequent rider surveys. Building upon recent work on the utilization of automated fare-collection
and vehicle-location systems for passenger-behavior analysis, this thesis presents
methods for inferring the full journeys of all riders on a large public transport
network.
Using complete daily sets of data from London’s Oyster farecard and iBus
vehicle-location system, boarding and alighting times and locations are inferred
for individual bus passengers, interchanges are inferred between passenger trips
of various public modes, and full-journey origin–interchange–destination
matrices are constructed, which include the estimated flows of non-farecard
passengers. The outputs are validated against surveys and traditional origin–
destination matrices, and the software implementation demonstrates that the
procedure is efficient enough to be performed daily, enabling transport providers to observe travel behavior on all services at all times.
Thesis Supervisor: Nigel H.M. Wilson
Title: Professor of Civil and Environmental Engineering
Thesis Supervisor: Harilaos Koutsopoulos
Title: Professor of Transport Science, Royal Institute of Technology
Thesis Supervisor: John P. Attanucci
Title: Lecturer & Research Associate, Department of Civil and Environmental
Engineering
Acknowledgements
The research that culminated in this thesis evolved over a three-year period, during which I was extremely fortunate to have the guidance of
multiple faculty members. Nigel Wilson and John Attanucci guided
this work from its inception. Their intuitive knowledge of transit,
standard of academic excellence, and complementary analytical styles
guaranteed stimulating and fruitful discussion at each weekly meeting.
Haris Koutsopoulos brought a similarly valuable perspective to the latter
half of the project: his expertise and ingenuity strengthened the methodologies of Chapters 3 and 4 while providing the guidance to overcome
the challenges addressed in Chapter 5. Haris and Nigel’s detailed reviews
of the manuscript greatly strengthened it—their interest and close attention are sincerely appreciated. Jinhua Zhao played a key role in guiding
the early stages of this work, and the vision that he instilled was in mind
throughout the project. Fred Salvucci kindly reviewed the manuscript
as well. His policy insights were invaluable as always, and I’ll miss his
thoughtful weekly provisioning of yucca donuts.
This work would not have been possible without the support of
Transport for London (TfL). Shashi Verma, Lauren Sager Weinstein, and
John Barry provided the invaluable opportunity to work with and learn
from their exceedingly talented teams, while their own feedback and
support for the project were truly encouraging. Andrew Gaitskell answered countless arcane technical questions and extracted mountains of
data. James McNair explored the algorithms and the code itself, lending
a welcome (and clever) second perspective in what had previously been
a solitary endeavor. Malcolm Fairhurst and Tony Richardson presented
an opportunity to test this research with a real-world problem, while
Duncan Horne, Mark Roberts, Carol Smales, Mark Butlin, David Warner,
Liam Henderson, Geoffrey Maunder, Dale Campbell, and Tim Cooper
shared expert insights about all aspects of TfL and its data.
The staff of London Buses were equally instrumental in this project.
Steve Robinson shared his unparalleled iBus expertise and, along with
Nigel Hardy, Lisa Labrousse, Aidan Daly, and George Marcar, generously and patiently answered complex technical questions. Steve and
Kevin Field tediously extracted large volumes of data, Annelies de Koning
created indispensable data extraction tools, and Bob Blitz and Fergus
McGhee shared their expert knowledge of the bus network. Alex Phillips
and Keith Gardner developed a user-requirements framework which
strengthened and refined the tools developed in this research. Special
thanks to Rosa McShane for her friendship and hospitality, and for always handling logistic and bureaucratic issues.
Additional support was provided by the Department of Civil and
Environmental Engineering through a Schoettler fellowship and a teaching assistantship, in which I had the rewarding experience of teaching
with and learning from George Kocur. Tomer Toledo gave valuable feedback on the scaling problem, while Ginny Siggia and Kris Kipp guided
me expertly through all manner of institutional hurdles.
This project grew from an initial collaboration with Albert Ng, whose
talents and creativity are as valued as his friendship. Mike Frumin generously sacrificed hours of thesis-writing time to share his (locally) unparalleled knowledge of London’s transport and information systems, while
Winnie Wang and Yossi Ehrlich similarly lent their code, data, and methodological insights. Kevin Muhs exemplified the virtue of patience with
his cheerful (and brave) de facto alpha testing, which yielded many valuable discoveries and suggestions (among many mutual tangential but welcome diversions). Gabriel Sánchez-Martínez shared his IPF source code,
and helped vet scaling algorithms through numerous stimulating whiteboard discussions (which also benefitted from the insights of Sam Hickey,
Nihit Jain, and Kari Hernandez). These and the other transit-lab students
of 2010–2013 made the countless hours in the office far more enjoyable.
Lastly, thanks to my family for their encouragement and support, and
to Dini for absolutely everything. The work in these pages was sustained
by your love and sacrifice.
Contents
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.1Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.1.1Full Journeys and Intermodal Travel . . . . . . . . . . . . . 19
1.1.2The State of the Art . . . . . . . . . . . . . . . . . . . . . . 19
1.1.3Application to London . . . . . . . . . . . . . . . . . . . . . 20
1.2Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 Public Transport in London . . . . . . . . . . . . . . . . . . . . . 23
2.1 London’s Public Transport Network . . . . . . . . . . . . . . . . 23
2.1.1London Buses . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1.2The London Underground . . . . . . . . . . . . . . . . . . 24
2.1.3National Rail . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.1.4London Rail . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1Oyster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.2iBus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.3Gateline and ETM . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.4Spatial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3Bus Origin and Destination Inference . . . . . . . . . . . . . . 31
3.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.1.1Inferring Bus Origins Using AFC and AVL Data . . . . . . . . 32
3.1.2Inferring Bus Destinations Using AFC and AVL Data . . . . . 33
3.2 Origin Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.1Exploratory Analysis of Input Data . . . . . . . . . . . . . . 35
3.2.2Origin-Inference Methodology . . . . . . . . . . . . . . . . 38
3.2.3Origin-Inference Results and Sensitivity Analysis . . . . . . 40
3.3 Destination Inference . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3.1Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.2Destination-Inference Methodology . . . . . . . . . . . . . 47
3.3.3Destination-Inference Results and Sensitivity Analysis . . . 52
3.4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4 Interchange Inference . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1 Concepts and Terminology . . . . . . . . . . . . . . . . . . . . . 60
4.2 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2.1Discerning Activities Using AFC Data . . . . . . . . . . . . . 62
4.2.2Interchange Inference Using AFC Data . . . . . . . . . . . . 62
4.3Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3.1Binary Conditions . . . . . . . . . . . . . . . . . . . . . . . 66
4.3.2Temporal Conditions . . . . . . . . . . . . . . . . . . . . . 68
4.3.3Spatial Conditions . . . . . . . . . . . . . . . . . . . . . . . 70
4.4 Results and Sensitivity Analysis . . . . . . . . . . . . . . . . . . . 73
4.4.1Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . 73
4.4.2Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4.3Comparison with the London Travel Demand Survey . . . . 82
4.5Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5 Full-Journey Matrix Expansion . . . . . . . . . . . . . . . . . . 85
5.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.2 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2.1Iterative Proportional Fitting on Closed Networks . . . . . 88
5.2.2Application of IPF on London’s Public Transport Network . 90
5.3 Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.4Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.4.1Problem Definition Revisited . . . . . . . . . . . . . . . . . 95
5.4.2Problem Formulation and Solution . . . . . . . . . . . . . . 98
5.5 Example: Test Network . . . . . . . . . . . . . . . . . . . . . . 101
5.5.1Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.5.2Comparison to Iterative Proportional Fitting . . . . . . . . 105
5.6 Application to the London Network . . . . . . . . . . . . . . . 108
5.6.1Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.6.2Validation Against Iterative Proportional Fitting on the
London Network . . . . . . . . . . . . . . . . . . . . . . . 112
5.7Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.1Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.2Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
6.2.1Step 1: Preprocessing and Nearest-Stop Calculation . . . . .118
6.2.2Step 2: Origin Inference and Daily History
Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . 120
6.2.3Step 3: Destination and Interchange Inference . . . . . . . 122
6.2.4Step 4: Full-Journey Construction . . . . . . . . . . . . . 123
6.2.5Step 5: Full-journey Matrix Expansion . . . . . . . . . . . 124
6.3 Results and Performance . . . . . . . . . . . . . . . . . . . . . . 126
6.4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
7Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.1 Bus Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7.1.1Boarding/Alighting/Flow Profiles and Route-Level
OD Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7.1.2Bus Passenger Travel Time and Distance . . . . . . . . . . 130
7.2 Cross-Modal and Full-Journey Applications . . . . . . . . . . . 132
7.3 Observing Changes in Rider Behavior and Demand . . . . . . . 134
7.4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
8.1 Summary and Findings . . . . . . . . . . . . . . . . . . . . . . . 139
8.2Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 141
8.3 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . 143
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
List of Figures
2-1 Schematic travel-zone map of London rail services . . . . . . . . . . . 26
2-2 Geographic map of London rail services . . . . . . . . . . . . . . . . . 27
2-3 Underground, National Rail, and DLR services in Central London . . . 28
3-1Oyster, BCMS, and iBus data for a portion of a single bus route . . . . . 36
3-2 Activity diagram of the origin-inference process . . . . . . . . . . . . 39
3-3 Distribution of origin-inference error for all Oyster bus boardings . . 42
3-4 Letter-designated bus stops near Elephant and Castle Tube station . . 45
3-5 Elephant and Castle stops S and T . . . . . . . . . . . . . . . . . . . . 45
3-6 Stop and station locations in Greater London . . . . . . . . . . . . . . 46
3-7 Activity diagram of the destination-inference process . . . . . . . . . 49
3-8 Example of destination inference . . . . . . . . . . . . . . . . . . . . 50
3-9 Distribution of average out-of-system speeds . . . . . . . . . . . . . . 53
3-10 Distribution of Euclidean distances between candidate alighting
stops and next fare transaction . . . . . . . . . . . . . . . . . . . . . . 55
4-1 Activity diagram of the interchange-inference process . . . . . . . . . 67
4-2 Two potential interchange locations yielding equal cumulative
Euclidean distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4-3 Interchange boundaries for various circuity ratios . . . . . . . . . . . 72
4-4 Time between cardholders’ successive journey stages . . . . . . . . . . 74
4-5 Distribution of elapsed times between candidate bus-stop arrivals
and subsequent bus boardings. . . . . . . . . . . . . . . . . . . . . . . 77
11
4-6 Distribution of circuity ratios for potential interchanges . . . . . . . . 78
4-7 Honor Oak Park to Bond Street via London Bridge . . . . . . . . . . 79
4-8 London Fields to Angel via Hackney or Liverpool Street . . . . . . . 79
4-9 Distribution of potential full-journey distances . . . . . . . . . . . . . 80
4-10 Distribution of inferred journey durations . . . . . . . . . . . . . . . 82
4-11 Full journeys per passenger per day . . . . . . . . . . . . . . . . . . . 83
4-12 Stages per journey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5-1 The test transit network . . . . . . . . . . . . . . . . . . . . . . . . . 86
5-2 IPF contingency table for the test network . . . . . . . . . . . . . . . . 89
5-3 Example of the control-total adjustment process . . . . . . . . . . . . 93
5-4 Example of estimated passenger flow on the test network . . . . . . . 97
5-5 Formulation and results of the full-journey scaling process,
as applied to the test network . . . . . . . . . . . . . . . . . . . . . . 102
5-6 Convergence of scaled transaction-node flows toward
control totals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5-7 Comparison of actual and estimated itinerary flows . . . . . . . . . . 104
5-8 Comparison of passenger flows for the test network . . . . . . . . . 107
5-9 Hourly control totals for Victoria Underground Station . . . . . . . 108
5-10 Histograms of scaling factors . . . . . . . . . . . . . . . . . . . . . . .110
5-11 Comparison of sample (or seed) flows to scaled flows . . . . . . . . . .111
5-12 Distributions of OD-pair scaling factors on London's rail network . . .112
5-13 Comparison of full-journey scaling and IPF by OD pair . . . . . . . . .113
6-1 Overview of processes, inputs, and outputs . . . . . . . . . . . . . . .116
6-2 Class diagram of origin-, destination-, and interchange-inference
package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117
6-3 Activity diagram of origin-, destination-, and interchange-inference
processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6-4 Activity diagram of full-journey matrix-expansion process . . . . . 125
7-1 Boarding/alighting/flow profile for Route 488 inbound . . . . . . . 129
7-2 Origin–destination matrix for Route 488 inbound . . . . . . . . . . 129
12
7-3 Comparison of average journey-stage lengths: GLBPS vs.
OD-inferred Oyster . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7-4 Comparison of average journey-stage lengths by bus route:
GLBPS vs. OD-inferred Oyster . . . . . . . . . . . . . . . . . . . . . . .131
7-5 Origins of full journeys ending at Oxford Circus station . . . . . . . 133
7-6 Frame from a time-lapse animation of a full day’s inferred
Oyster activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
7-7 First origins of day for riders of route 205x . . . . . . . . . . . . . . 135
7-8 Boarding/alighting/flow profile for route 488, before and after
its extension to Dalston Junction . . . . . . . . . . . . . . . . . . . . 137
7-9 Origin–destination matrix for the extended Route 488 inbound . . . 137
13
List of Tables
3-1 Detailed origin-inference results, ten-day average . . . . . . . . . . . . 41
3-2 Summary of origin-inference results . . . . . . . . . . . . . . . . . . 43
3-3 Cumulative frequencies of observed inter-stage distances . . . . . . . 55
3-4 Destination-inference results . . . . . . . . . . . . . . . . . . . . . . . 57
4-1 Median elapsed times between Oyster cardholders’ consecutive bus
boarding transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4-2 Tests applied during the interchange-inference process . . . . . . . . . 66
4-3 Interchange-inference results: ten-day average . . . . . . . . . . . . . 81
5-1 London Underground weekday time periods . . . . . . . . . . . . . . 91
5-2 Satisfaction of farebox and station-gate control totals . . . . . . . . . 109
14
List of Acronyms
AFC Automatic fare collection
APC Automatic passenger counter
ATOC Association of Train Operating Companies
AVL Automatic vehicle location
BCMS Bus Contract Management System
BODS Bus Origin–Destination Survey
DLR Docklands Light Railway
ELL East London Line
ETM Electronic ticket machine
GLBPS Greater London Bus Passenger Survey
IPF Iterative proportional fitting
LATS London Area Travel Survey
LTDS London Travel Demand Survey
MBTA Massachusetts Bay Transportation Authority
MLE Maximum likelihood estimate
NLC National location code
ODOrigin–destination
ODXOrigin–destination–interchange
OSI Out-of-station interchange
RODS Rolling Origin–Destination Survey
TfL Transport for London
15
16
1
Introduction
Urban public transport providers have historically planned and managed
their networks and services with limited knowledge of their customers’
travel patterns. Unlike airlines or interurban rail providers, who typically
issue tickets specifying distinct origins, destinations, and transfer points—
often in designated vehicles and seats—the nature of urban transit operations has necessitated fare-payment schemes which yield only aggregate
ridership data, at resolutions no finer than that of the station gate or bus
farebox.1 Put simply, transit agencies have not been able to easily observe
where their passengers travel.
Farebox and station-gate data are useful for describing ridership from
the perspective of the transit system, but passenger-centric data, such
as the origins, destinations, and transfer points of individual customers,
have traditionally been collected only through costly surveys (which are
therefore conducted infrequently using small rider samples). The growing adoption of automated data-collection systems, however, is providing transit operators with vast stores of disaggregate data, which can be
manipulated to reveal travel information from a rider-focused perspective.
But the distillation of passenger-centric information is not trivial, as most
automated data-collection systems—namely automated fare collection
(AFC), automated vehicle location (AVL), and automated passenger coun-
1 High peak-period passenger volumes, high but often variable service frequencies, and
dense, complex networks have led most transit agencies to adopt flat or zonal fare policies,
designed to expedite fare processing and maximize passenger throughput.
17
ter (APC)—have been designed for other purposes: their applicability to
rider-perspective analysis is mostly serendipitous.2
This thesis builds upon recent work in the synthesis of passenger-centric public transit information and demonstrates the feasibility of processing the complete set of data for a large multi-modal transit provider
in a way that is suitable for execution on a daily basis. Using London’s
transit network as an example, origins and destinations are inferred for
bus journey stages, transfers between stages of various modes are inferred,
and a multimodal origin–interchange–destination matrix of full journeys
is constructed, including estimated flows for non-AFC passengers. Applications are presented at various spatial and temporal scales from both
the customer and system perspectives, including applications for service
planning, rider behavior analysis, and project impact analysis.
The remainder of this chapter elaborates on the motivation for this
research, presents its specific objectives and the approach taken, and outlines the structure of this document.
1.1
Motivation
Passenger-centric transit information has historically been costly to capture. Origin–destination (OD) information for an the individual bus route
could be estimated at the stop level from manual boarding counts combined with a sample of rider surveys (Ben Akiva et al 1985) or, at the
expense of accuracy, with boarding counts alone (Simon and Furth 1985,
Mishalani et al 2011). As survey response rates continue to decline, however, increasing their cost and bias (Stopher 2008, qtd. in Simon 2010),
AFC data (supplemented in many cases by AVL data) have been shown to
provide similar OD information at larger scales and at lower cost (Park et
al 2008). A similar approach can be taken for rail networks requiring both
exit and entry transactions, since AFC transactions can be associated by
card number (Gordillo 2006, Chan 2007).
2 AFC systems, which read magnetic-stripe or RFID “smart” cards containing transit passes or
credit, were designed to streamline fare collection. AVL systems track vehicle locations for
the onboard announcement of stop information and to provide control centers with realtime fleet-management capability. APC systems track aggregate boardings and alightings but
are not directly linked to specific passengers or origin–destination pairs.
18
1.1.1
Full Journeys and Intermodal Travel
In addition to relating entry and exit records, AFC data can be used to associate all of a cardholder’s daily transactions, enabling transit providers
to more easily discern interchanges from activities in order to infer customers’ true origins and destinations. While analysis of the resultant full
journeys and daily travel histories can provide valuable information for
transport planners and operators, interchanges themselves are worthy of
study because of their influence on customers’ travel decisions (Wardman
and Hine 2000, Hine and Scott 2000, Guo et al 2007, Eom et al 2011) and
their role in enabling intermodal travel (Institute of Logistics and Transport 2000, Seaborn et al 2009, Transport for London 2002 & 2009).
The acceptance of AFC cards on multiple modes allows the observation of intermodal transit journeys, which are becoming increasingly important to transit providers and policy makers as a means of improving
mobility through the elimination of barriers to cross-modal travel. Describing London’s public transport strategy at the turn of the twenty-first
century, then-mayor Ken Livingstone argued that “[modal] integration
is not just about making the public transport network more attractive to
existing and potential passengers, it is also about how the transport system can contribute to the achievement of broader economic, social, and
environmental objectives” (Transport for London 2001). The economic
benefits of increased mobility are supported by the work of Krizek (2003)
and by Anas (2007), who notes that time savings from chained trips tend
to be reinvested in additional travel and activities, yielding an overall increase in utility. London’s exceptionally large, dense transit network and
the acceptance of the Oyster smart card on the region’s several historically
independent but now unified transit modes provides a valuable opportunity for intermodal integration and its monitoring through automatically
collected data.
1.1.2
The State of the Art
Several scholars and practitioners have advanced the application of automatically collected transit data in the past decade. Building upon earlier
work, Chu and Chapleau (2008) infer full passenger journeys for a bus
network, then analyze multiple days of AFC data to estimate general activity types for cardholders’ recurrent journeys (2010). Farzin (2008) auto19
mates the majority of the destination-inference process on a portion of
a large bus network and constructs a zonally aggregated OD matrix. Munizaga et al (2011) automate the inference of origins and destinations for
a large multi-modal network and, using a simple interchange-inference
algorithm, construct a full-journey origin–destination matrix for AFC
cardholders.
The synthesis and refinement of previous research, coupled with more
robust and efficient processing algorithms, holds promise for the prospect
of implementing passenger-centric data analysis in ways that can be applied by transit operators on a daily basis.
1.1.3
Application to London
Utsunomiya et al (2006) conclude that the market penetration of AFC cards
is critical to the accuracy of passenger-centric analyses (2006). With penetration rates of over 90 percent on buses and 80 percent on the London
Underground system, Oyster’s six million daily bus boardings and five
million station entries provide an exceptionally large sample from one of
the world’s foremost transit networks. Transport for London (TfL), the
transportation agency for the Greater London metropolitan area, oversees
the city’s transit system and maintains complete databases of recent AFC,
AVL, and ancillary transit data. TfL’s data have been used to construct OD
matrices for the gated Underground system (Gordillo 2006, Chan 2007),
one of its ungated light rail systems (Henderson 2010), and for individual
bus routes (Wang et al 2011). The data have also been used to track service
reliability and to inform service-planning proposals (Chan 2007, Uniman
et al 2010, Ehrlich 2010, Frumin 2010).
TfL’s data sources have recently been used to assess the impacts of new
capital projects (Ng 2011, Muhs 2012), fare policy changes ( Jain 2011), and
service-reliability metrics (Schil 2012).3 The incorporation of previous
London research with methodologies from other case studies presents an
opportunity to extend traditional analyses to the observation of travel
patterns before and after the introduction of new (or the alteration of
existing) public transport services in London. And by developing an efficient and flexible way to repeat and extend such analyses, TfL can be
3 Muhs (2012) and Schil (2012) used the tools and algorithms developed in this thesis for part
of their analyses.
20
equipped to internally analyze rider behavior during upcoming events
such as the city’s hosting of the 2012 Olympic and Paralympic Games and
the launch of Crossrail service later in the decade.
1.2
Objectives
This research tests the hypothesis that a flow matrix of full intermodal
passenger journeys can be estimated flexibly and efficiently using entire
populations of automatically collected data from a large public transit
network, in a way that can be performed by a transit provider on a daily
basis. More specifically, this thesis seeks to fulfill the following objectives:
·· Infer boarding and alighting locations and times for bus journey stages. Bus AFC
transactions typically include service information and boarding times but
lack spatial information, while alighting information is not recorded at
all. Error-correction algorithms should be applied to ensure the highest
possible origin- and destination-inference rates.
·· Infer interchanges between journey stages of any AFC-enabled mode. Infer interchanges as accurately as possible, taking into account observed or inferred access and egress distances, observed bus headways, path circuity,
and transfer time.
·· Generate an intermodal full-journey origin–interchange–destination matrix covering all modes in the transit network for which data are available. Generate counts
of each unique itinerary observed to have been taken by one (or more)
passengers, where itinerary is defined as a unique combination of origin,
destination, and transfer locations. Expansion factors, which are used to
scale itinerary flows to account for unobserved travelers, should be estimated in such a way that the counts of their constituent journey stages
satisfy the various control totals that are available for each transit mode.
·· Conduct all inference and expansion processes at the finest possible level of disaggregation. Doing so will afford downstream processing and reporting tools
the flexibility to choose a level of aggregation appropriate for a given task.
21
·· Allow parameters to be adjusted at runtime. Bagchi and White (2003, 2004) note
the importance of calibrating the parameters of rule-based processes in
accordance with empirical observations or surveys, while Timmermans
et al (2002) show that travel parameters vary across networks and societies.
Parameters should be adjustable by the user and should not be embedded
in code.
·· Streamline the processing tools so that they can be run on a daily basis. Tools should
be able to complete their processing overnight, and should ideally run in
less than 30 minutes so that users can more easily experiment with different parameter values.
·· Demonstrate applications. Applications of each process should be demonstrated (e.g., bus journey stages, full journeys, and the expanded OD matrix), from both the operational and passenger perspectives, and at various
levels of aggregation.
1.3
Thesis Organization
The methods developed in this thesis can be grouped into three categories: bus origin and destination inference, interchange inference, and fulljourney matrix expansion. Since each category has a distinct methodological background, each is presented in its own chapter. Following an
overview of London’s public transport system and operations in Chapter
2, the three phases of the problem are presented in chapters 3, 4, and 5,
with each including a review of the relevant literature and a discussion of
their validation and results. The technical implementation of the methods is discussed in Chapter 6 followed by a demonstration of applications
in Chapter 7. Chapter 8 reflects on the study’s findings and presents its
conclusions and recommendations.
22
2
Public Transport in London
The methods presented in this thesis were developed and tested using
data from London’s public transport network. This chapter describes the
network’s structure and services, and introduces the data sources used to
conduct this research.
2.1
London’s Public Transport Network
London’s transit network is planned and managed—and partially operated—by Transport for London (TfL), a government body serving the
Greater London metropolitan area and led by a 17-member board appointed and chaired by the Mayor of London (Transport for London
2011). TfL oversees most transportation services in Greater London, including public transport, taxis, traffic management (including congestion
charging in the city center), river services, bicycle sharing, and paratransit.
2.1.1
London Buses
London Buses, a subgroup of TfL’s Surface division, plans and manages the metropolitan area’s network of over 800 routes and 21,000 stops,
which provide roughly six million passenger trips daily. London’s fleet of
over 8,500 iconic red double-decker (and single-deck) buses is operated by
private companies, who bid for contracts awarded by the agency (Transport for London 2011, 2012).
23
Bus riders are charged a flat fare, regardless of travel location or time of
day (although some riders are eligible for discounts). Fares can be paid in
cash on board (or compulsorily at ticket vending machines at some Central London bus stops) or riders can tap their Oyster cards upon boarding,
to deduct credit or validate travel passes stored on the card.
2.1.2
The London Underground
The Underground, or Tube, is London’s metro system, offering high frequency service on 11 lines, several of which include multiple branches
(see figures 2-1 and 2-2).1 Underground fares are assessed according to the
geographic travel zone of the rider’s starting and ending locations (Figure
2-1), requiring users to validate their fare media upon both entry and exit.
The Underground system is often at capacity during peak hours, and
relieving this congestion is a major goal of many of TfL’s fare policies and
capital projects. Higher fares are charged during peak periods to incentivize off-peak travel, while recent and current projects such as the East
London Line Extension and Crossrail were designed in part to provide
alternatives to Underground service at key bottlenecks.
2.1.3
National Rail
National Rail is the brand name of the Association of Train Operating
Companies (ATOC), a group of 24 private firms that provide commuter
and intercity rail services throughout Great Britain. Several National
Rail lines serve London, but all terminate outside (or slightly within)
the bounds of the Circle Line, the quasi-orbital Underground line that
loosely defines the city’s center (see Figure 2-3). Much of the congestion
on the Underground is due to passengers transferring between National
Rail and the Tube during peak periods, and stations serving both Underground and National Rail lines exhibit some of the highest passenger
volumes in London.
1 Although the term Tube originally referred to the seven deep-bore rail lines drilled into the
earth (thus exhibiting the cylindrical tunnels and passageways that inspired the nickname),
the term is now typically used to refer to both the deep-bore lines and the four “subsurface” lines, which were excavated directly from the streets above using the “cut-and-cover”
technique.
24
2.1.4
London Rail
In addition to the Underground, TfL provides rail service on the London
Overground, Docklands Light Railway (DLR), and Tramlink. All three
modes are planned and managed by TfL’s London Rail group, which also
liaises with ATOC to coordinate National Rail services within Greater London (Transport for London 2011).
The Overground system comprises a series of rail lines primarily in
travel zones two through four, which are being developed into a suburban orbital network encircling most of the Underground system. Some
tracks are shared with National Rail and Freight services, and many stations were acquired from National Rail.2
The Docklands Light Railway is an automated system operating on
dedicated rights of way in the redeveloped Docklands area, one of Greater
London’s three primary commercial cores. DLR services connect with Underground and Overground lines in East London, and one branch extends
into another commercial core in the City of London.
The Tramlink system consists of three light rail lines serving Croydon,
the southernmost borough in Greater London. Tramlink connects with
Underground and National Rail services and, with the opening of the
extended East London Line, the Overground network.
2.2
2.2.1
Data Sources
Oyster
The Oyster card is accepted on most TfL transport modes and typically
registers 16 million transactions daily, or roughly 10 million passenger
trips. Oyster use is incentivized by reduced fares, designed to increase
passenger throughput by streamlining fare validation at station gates and
bus fareboxes.
2 A notable exception is the East London Line, a former Underground service that was extended along a combination of disused above-grade rail viaducts and newly acquired rights
of way (Ng 2011, Muhs 2012).
25
Towards
Aylesbury
ZONE
Central
Circle
District
Towards
Hemel Hempstead
Chesham
Bakerloo
ZONE
9
Amersham
ZONE
8
8
ZONE
limited service
Hammersmith & City
Chalfont
& Latimer
Jubilee
Metropolitan
Towards
High
Wycombe
Northern
Piccadilly
7
Watford
Rickmansworth
Uxbridge
First Capital Connect
Ruislip
Gardens
Southern
Southeastern
Harrowon-the-Hill
Sudbury &
Harrow Road
Sudbury Hill
limited service
ZONE
3
Golders Green
Cricklewood
Willesden
Green
Wembley
Stadium
South West Trains
Brondesbury
Park
Perivale
2
Queen’s Park
Riverboat services
Emirates Air Line
(opening summer 2012)
Kilburn Park
Maida Vale
Warwick
Avenue
South Greenford
Station in both fare zones
Tramlink fare zone
Hanger Lane
Emirates Air Line fare zone
6
West
Drayton
ZONE
Park Royal
ZONE
5
4
Hayes &
Harlington
Drayton Green
Southall
ZONE
West
Ealing
Ealing
Broadway
2
Latimer
Road
North
Acton White City
Acton
Main
Line
Towards
Slough
Shepherd’s
Bush Market
South
Acton
Chiswick
Park
Hammersmith
Turnham Stamford Ravenscourt
Park
Green
Brook
High Street
Kensington
Kensington
(Olympia)
Bond
Street
Gloucester
Road
West
Kensington
Earl’s
Court
Brentford
Richmond
Barnes
Mortlake
Putney
Southfields
Wimbledon Park
Twickenham
Queenstown
Road (Battersea)
Wandsworth
Town
Clapham High Street
Clapham North
Earlsfield
Thames
Ditton
Surbiton
N u nhe a d
Colliers Wood
Tooting
Tulse
Hill
West
Norwood
Norbury
Mitcham
Eastfields
Thornton Heath
Beddington
Lane
Morden South
Mitcham
Junction
St. Helier
Sutton Common
West Sutton
Therapia
Lane
Waddon
Marsh
Selhurst
3
Ampere
Way
Penge East
Kent House
Emirates Air Line
(opening summer 2012)
Custom House
for ExCeL
Station in both fare zones
Tramlink fare zone
Emirates Air Line fare zone
Prince Regent
Royal Albert
Beckton Park
Cyprus
Gallions Reach
Tow
Slo
Carshalton
Carshalton
Beeches
ZONE
4
W o o dm a n s t e r n e
Kingswood
Tadworth
Epsom Downs
Coulsdon Town
Coulsdon South
Towards
Gatwick Airport
Slade Green
Kidbrooke
Falconwood
Towards
Ebbsfleet
International
Bexleyheath
Welling
Barnehurst
Catford Bridge
Be c k e nha m
Junction
Albany
Park
New Eltham
Mottingham
C r ay f o rd
Sidcup
Bexley
Towards
Dartford and
Gravesend
Grove Park
Elmstead
Woods
Sundridge Park
Bromley North
Bi c k l e y
Bromley
South
Chislehurst
St . Mar y C ray
Petts
Wood
Towards
Swanley and
Sevenoaks
Chelsfield
Heat
Term
Knockholt
Towards
Sevenoaks
Coombe Lane
Gravel Hill
Addington Village
Fieldway
Sanderstead
Tow
Sh
King Henry’s Drive
Kenley
Whyteleafe
Whyteleafe South
Tow
Sta
Lloyd Park
South Croydon
Riddlesdown
New Addington
Upper Warlingham
Caterham
Tattenham Corner
Erith
Sandilands
Purley Oaks
R e e dh a m
6
Belvedere
Abbey
Wood
Orpington
Clock House
Hayes
ZONE
Renamed
Coulsdon Town
on 12th April.
Woolwich
Arsenal
West Wickham
Addiscombe
East
Croydon
5
Plumstead
Charlton
Eden Park
Lebanon
Road
Church
Street
Beckenham
Road
Elmers End
Blackhorse Lane
Wellesley Road
Purley
Cheam
Avenue
Road
Birk be c k
Arena
ZONE
Woolwich
Dockyard
Hither Green
New Beckenham
Ravensbourne
Harrington
Road
Norwood
Junction
West Croydon
Wallington
Maze
Hill
Shortlands
Anerley
Centrale
Westcombe
Park
Eltham
Penge West
Woodside
Reeves
Corner
Hackbridge
Chipstead
Towards
Epsom and
Dorking
6
King George V
Beckenham Hill
Lower Sydenham
Gipsy Hill
Belmont
Banstead
Catford
Crystal
Palace
Wandle Park
Waddon
Sutton
Ewell East
Emirates
Greenwich
Peninsula
Bellingham
George Street
Ewell West
Riverboat services
ZONE
Beckton
Lee
Sydenham
Hill
Streatham
Common
Mitcham
Chessington South
Towards
Guildford
South West Trains
Grays
London City Airport
Ladywell
Crofton Park
Sydenham
West
Dulwich
Streatham
Hill
Belgrave Walk
South Merton
Towards
Woking and
Guildford
5
Elverson Road
ZONE
Phipps Bridge
Stoneleigh
Royal
Victoria
Emirates
Royal Docks
Lewisham
Forest Hill
Herne Hill
Morden Road
Chessington North
ZONE
4
Pontoon Dock
Blackheath
Brockley
Brixton
Tooting
Broadway
Worcester
Park
Purfleet
Airport
ZONE
Deptford Bridge
New Cross Gate
North Dulwich
Malden
Manor
Rainham
limited service
Southeastern high speed
Honor Oak Park
Streatham
Tolworth
New Cross
Peckham Rye
Balham
Morden
Hampton
Court
De nma r k H i l l
South Wimbledon
Berrylands
Dagenham
Dock
limited service
Interchange stations
West
Silvertown
Greenwich
St. Johns
East Dulwich
Clapham South
Tooting
Bec
Motspur
Park
Canning
Town
Cutty Sark for
Maritime Greenwich
Deptford
Queen’s Road Peckham
Clapham
Common
Wandsworth
Common
Wimbledon
Chase
N orbit on
2
Kennington
Stockwell
Loughborough
Junction
Merton Park
New Malden
Kingston
Oval
Wandsworth Road
Raynes Park
Hampton
Wick
Surrey Quays
Southeastern
Towards
Southend
3
East
India
Heron Quays
South Quay
Crossharbour
Mudchute
Island Gardens
South Bermondsey
Dundonald
Road
Towards
Shepperton
Teddington
Canada Water
Southern
Archway
Canary Wharf
London
Bridge
London Midland
East Ham
North
Greenwich
for The O 2
Elephant & Castle
Wimbledon
Strawberry
Hill
Poplar
Rotherhithe
Lambeth
North
Heathrow Express
Becontree
ZONE
Star Lane
Blackwall
ZONE
Haydons
Road
West Ham
West India
Quay
River Thames
Heathrow Connect
Chafford
Hundred
Lakeside
Vauxhall
Battersea
Park
Whitton
Fulwell
Fenchurch Street
Upton
Park
Plaistow
Langdon Park
Limehouse
Tower
Gateway
Tower
Hill
Borough
Clapham Junction
East Putney
Hounslow
Hampt on
Temple
Bromley
by-Bow
limited service
First Great Western
Greater Anglia
Woodgrange
Park
Upney
All Saints
Southwark
Waterloo
and
Waterloo East
River Thames
North
Sheen
St. Margarets
Monument
Putney Bridge
Barnes Bridge
Heathrow
Terminal 4
Cannon
Street
First Capital Connect
Ockendon
Barking
Westferry
Shadwell
Forest
Gate
Devons Road
Aldgate
Wapping
Pimlico
Imperial
Wharf
Kew Gardens
2
Whitechapel
Bermondsey
Parsons Green
Syon Lane
Isleworth
Aldgate
East
Mansion
House
Blackfriars
Embankment
Fulham
Broadway
Chiswick
Bank
Bow
Road
Stepney Green ZONE
1
St. Paul’s
City
Thameslink
Mile End
c2c
Hornchurch
Dagenham
Heathway
Abbey Road
Bow
Church
ZONE
Leicester
Square
St. James’s
Park
Maryland
Stratford
High Street
Shoreditch
High Street
Westminster
Victoria
South
Kensington
Liverpool
Street
Chiltern Railways
Dagenham East
Ilford
Manor Park
Stratford
Pudding
Mill Lane
Bethnal
Green
Moorgate
Chancery
Lane
Charing
Cross
Sloane
Square
Hoxton
Upminster
Upminster
Bridge
Chadwell
Heath
Goodmayes
Wanstead Park
Bethnal Green
Old Street
Russell
Square
Covent
Garden
Piccadilly
Circus
Hackney
Wick
Homerton
Gants Hill
Leytonstone
High Road
Leyton
Cambridge Heath
Haggerston
Barbican
Holborn
Green Park
London Fields
Dalston
Junction
Wanstead
Leytonstone
Seven Kings
Essex Road
Goodge
Street
Tottenham
Court Road
Hyde Park Corner
Barons
Court
Highbury &
Islington
Angel
Euston
Square
Oxford
Circus
Knightsbridge
Gunnersbury
Kew
Bridge
Feltham
King’s
Cross
Hackney
Central
Emirates Air Line
Emerson
Park
Romford
Elm Park
Stratford
International
London Overground
London Tramlink
Redbridge
Walthamstow
Queen’s Road
Clapton
Docklands Light Railway
Newbury Park
Leyton
Midland Road
Hackney
Downs
Dalston
Kingsland
Canonbury
Farringdon
Warren
Street
Marble
Arch
West
Brompton
Hatton
Cross
Towards
Staines
Queensway
Drayton
Park
Caledonian
Road &
Barnsbury
Euston
Regent’s
Park
Lancaster
Gate
Notting
Hill Gate
Holland
Park
Hounslow
Central
Heathrow
Terminal
5
Great
Portland
Street
Edgware
Road
1
Bayswater
Shepherd’s
Bush
Hounslow
East
Hounslow
West
Caledonian Road
St. Pancras
International
Northfields
Osterley
Heathrow
Terminals
1, 2, 3
Baker Street
Marylebone
Westbourne Park
Ladbroke Grove
Goldhawk Road
Acton
Town
South
Ealing
Boston Manor
Swiss Cottage
Mornington
Crescent
Camden
Road
St. John’s Wood
Paddington ZONE
East
Acton Wood
Lane
Acton
Central
Ealing
Common
SPECIAL FARES APPLY
Oyster PAYG and
Travelcards are not
valid on
Heathrow Connect
between
Hayes & Harlington
and Heathrow Airport
or on Heathrow Express.
South
Hampstead
Edgware
Road
ZONE
3
West
Acton
North Ealing
Hanwell
Camden Town
Royal Oak
Castle Bar Park
ZONE
Kilburn
High Road
Holloway Road
Chalk Farm
Finchley Road
ZONE
Kensal Green
Airport
Arsenal
Walthamstow
Central
St. James
Street
SPECIAL FARES APPLY
Oyster PAYG and
Travelcards are not
Rectory Road
valid on Southeastern
Finsbury Park high speed.
For details, visit
southeasternrailway.co.uk
Gidea Park
Snaresbrook
Stoke Newington
Upper Holloway
Kentish Town
Belsize Park
West Hampstead
Brondesbury
Kensal Rise
Kentish Town
West
Waterloo & City
Towards
Southend and
Shoeburyness
Fairlop
Barkingside
South
Tottenham
Stamford Hill
Gospel Oak
Finchley Road
& Frognal
Alperton
Seven Sisters
Victoria
Harold Wood
Hainault
South Woodford
Blackhorse
Road
Tottenham
Hale
Tow
Hig
Wy
Northern
Grange Hill
Northumberland
Park
Bruce Grove
Manor
House
Hampstead Heath
Kilburn
Willesden Junction
Greenford
Interchange stations
Crouch Hill
Tufnell Park
Hampstead
Woodford
Wood Street
Harringay
Green Lanes
Metropolitan
Chigwell
Piccadilly
Angel Road
Turnpike Lane
Jubilee
Towards
Shenfield and
Southend Airport
Highams Park
White Hart Lane
limited service
Hammersmith & City
Roding
Valley
Silver Street
Wood Green
Hornsey
District
Buckhurst Hill
Chingford
Harringay
Archway
Circle
Loughton
Ponders End
Bowes Park
Highgate
Central
Southbury
Palmers Green
Alexandra Palace
Bakerloo
Epping
Brimsdown
Edmonton Green
Brent Cross
Hendon
Dollis Hill
Harlesden
Southeastern high speed
Archway
Wembley Park
Wembley Central
Sudbury Town
Enfield Lock
Theydon Bois
Winchmore Hill
Arnos Grove
Oakleigh Park
East Finchley
Stonebridge Park
limited service
Turkey Street
Bush Hill
Park
Finchley Central
Neasden
North Wembley
Towards
Cheshunt, Hertford East and Stansted Airport
Debden
Grange Park
Mill Hill East
Hendon Central
Preston
Road
Oakwood
New Southgate
Colindale
Kingsbury
Northwick
Park
Sudbury Hill
Harrow
Northolt
Park
Northolt
4
South Kenton
Greater Anglia
London Midland
Kenton
Enfield Chase
West Finchley
Mill Hill Broadway
Burnt Oak
West Harrow
South Harrow
First Great Western
ZONE
Canons Park
Towards
Cheshunt
Enfield
Town
Southgate
Queensbury
Rayners Lane
Gordon Hill
Bounds Green
Stanmore
Harrow & Wealdstone
North Harrow
Eastcote
South Ruislip
Heathrow Express
New Barnet
Totteridge & Whetstone
Edgware
Headstone Lane
Pinner
Ruislip
Manor
Heathrow Connect
High Barnet
Woodside Park
Ruislip
Ickenham
limited service
5
Hatch End
Northwood
Hills
Emirates Air Line
c2c
Hadley Wood
ZONE
Northwood
London Tramlink
Chiltern Railways
Elstree & Borehamwood
Crews Hill
Cockfosters
Bushey
Moor Park
Hillingdon
London Overground
6
Carpenders Park
West Ruislip
Docklands Light Railway
7
8
Towards
Hertford North
ZONE
Croxley
Victoria
Waterloo & City
ZONE
Watford High Street
Chorleywood
Towards
Welwyn Garden City
Towards
Luton Airport Parkway
ZONE
Watford Junction
Towards East Grinstead
and Uckfield
Figure 2-1. Schematic travel-zone map of London rail services (source: TfL).
A11
2
M1
1
A1
M1
1
A1
04
13
A11
2
A10
55
A1
04
A10
55
A1
037
13
1
1
M1
1
A1
23
A1199
A1
2
A11
2
A11
2
A10
4
M1
A1055
A1055
A1
037
A10
4
A1
2
A10
10
A10
10
A10
M1
1
A10
A10
A10
5
A10
5
A 1000
A 1000
A 10
A10
00
A5
64
M1
)
)
A1
9
A40
9
A40
A1
Emerson
Park
A127
Gidea
Upminster
A12
4
Park Upminster
I
5
A12
West
Horndon
A11
06
06
A1
0
0
A102
0
A10
0
A10
2
A23
Whyteleafe
5
M2
K
N E N
T
T
Knockholt
Jn4
Shoreham
Shoreham
0
Upper
Warlingham
0
Woldingham
A23
0
23
A23
4 Miles
Dunton
Green
6 Kilometres
2
A2
Caterham
M25
Bat &
Ball
M25
Docklands Light Railway
London Overground
under construction
Tramlink
M26
Dunton
Green
Docklands Light Railway
Southeastern
Maidstone and
Ashford International
Tramlink
London Connections
Bat &
Ball
Jn5
Southern
East Grinstead
and Uckfield
Southeastern
Maidstone and
Ashford International
Otford
Jn5
M25
A22
M25
3
A233
Woldingham
Jn6
Jn7
2
Southern
Crossrail
South West Trains
under construction
M26
A22
Whyteleafe
South
Caterham
6 Kilometres
National
Express
East Anglia
South West
Trains
Southeastern
Interchanges
under construction
Otford
3
Heathrow
Express
Southeastern
London
Midland
Southern
Interchanges
London Overground
Crossrail
under construction
4 Miles
2
0
Whyteleafe
South
Upper
Warlingham
Whyteleafe
M2 5
0
A22
M25
A21
A2022
A2
M2 5
0
A22
X
L
Y E Y
E
E
B
M2
5
15
A2
A 235
X
A23
A 235
E
New Addington
E
A217
Jn4
M25
A217
Knockholt
K
A2
17
A2
2
A205
H
T
U
S 15
O
A2
A3
17
A23
4
A2
17
A2
A23
4
A2
4
A2
A2
17
A3
A3
A23
A23
2
M25
A21
Chelsfield
Fieldway
c2c
Heathrow Connect
First
Capital Express
Connect
Heathrow
First
GreatMidland
Western
London
Heathrow
National Connect
Express East Anglia
Eynsford
M25
A24
20
Chelsfield
A233
A24
A23
A219
A219
A2
4
A307
A307
A243
A243
M
Eynsford
Orpington
A2
c2c (cased in black. All routes are shown
excluding
limited services)
First Capital
Connect
Chiltern
Railways
First Great
Western
20
0
Y
1
A2
A243
National Rail lines
M
M2
Kenley
Riddlesdown
Kenley
Southern
Redhill, Gatwick Airport,
Brighton, Eastbourne
and Worthing
Chiltern Railways
1
A243
L
A205
A
W
A3
A3
8
A30
A30
8
A311
B
K
W
H
T
U
K
R
S
A3
6
A30
O
16
A3
16
R
A
A3
6
A30
7
12
A311
A244
A102
A 10
07
A4
06
A4
7 A 4127
12
A 3005
A4
A 3005
A4
N
U
O
A244
N
H
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A244
H
16
A3
16
A3
es
am
Th
A244
er
R iv
es
am
Th
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M25
06
A1
A 10
07
A4
06
A4
A4127
A 4127
A312
A312
A408
M25
A11
A1
06
A 10
A1
A312
A4127
A437
A437
A408
H
M25
A3044
R iv
A20
2
Coulsdon
South
(cased in black. All routes are shown
Interchanges
excluding limited services)
20
Orpington
A233
Purley
Rail lines
WaterlooNational
& City
Southeastern
Chatham, Margate,
Canterbury
and Dover
M
Jn3
A2
A233
17
A21
A3
A21
Y
King Henry’s Drive
22
Piccadilly
Interchanges
Victoria
Swanley
Petts
Wood
Addington Village
A20
Sanderstead
Riddlesdown
Purley
Oaks
A2022
Farningham
Road
Jn3
A20
32
Sevenoaks
September 2011
Southeastern
Tonbridge, Hastings,
Ashford, Canterbury,
Folkestone and Dover
© Transport for London
A23
Southern
Redhill, Gatwick Airport,
Brighton, Eastbourne
and Worthing
Jn6
Jn7
M25
Southern
East Grinstead
and Uckfield
London Connections
September 2011
M25
2
First Capital Connect
Gatwick Airport
and Brighton
A2
Guildford, Havant,
Portsmouth and
Isle of Wight
M25
23
M
Jn8
Southern and
South West Trains
Dorking and
Horsham
A2
Metropolitan
Victoria
Northern
Waterloo & City
A225
A21
R
M
32
A2
E
A2
Hammersmith
& City
Northern
Jubilee
Piccadilly
Southeastern
Chatham, Margate,
Canterbury and Dover
Swanley
St. Mary
Cray
L
Circle
Jubilee
District
Metropolitan
A225
A21
17
M
A3
A
A3
H
B
New Addington
0
Petts
Wood
M
Northfleet
A2
St. Mary
Cray
E
Northfleet
London
GreenhitheUnderground lines
Circle
Stone
Swanscombe
Crossing
Bakerloo
District
A2
Central
Hammersmith & City
Jn2
Farningham
Road
L
Swanscombe
Central
M25
A21
I S
O
A2
32
O
Bakerloo
Key
to lines
M25
M
W
A23
A3
A 221
A
E
R
A 206
A 221
A2
H
L
B
Southeastern
Ashford International and Kent
Jn2
A2
2
c2c
Tilbury and
Southend
Grays
London
GreenhitheUnderground lines
Stone
Crossing
Southeastern
Gravesend
and Chatham
0
A22
Southeastern
Ashford International and Kent
Key to lines
Dartford
Crayford
c2c
Tilbury and
Southend
Grays
Chafford
Hundred
Jn31
A 206
A2041
M25
A2041
09
A2
A102
A21
I S
Gravel
Hill
09
A2
W
Coombe
Lane
M
First Capital Connect
Gatwick Airport
and Brighton
Hayes
Fieldway 2
2
20
KingAHenry’s Drive
22
A20
Chipstead
Kingswood
M25
E
A23
7
A23
Jn9
A217
Southern and
South West Trains
Dorking and
Horsham
A23
7
Kingswood
32
2
02
Addington Village
Gravel
Hill
Lloyd
Park
Purley
Oaks
Purley
A217
South West Trains
Guildford
A23
A23
Tattenham
Corner
Tadworth
5
South West Trains
Leatherhead
Guildford
L
14
A24
Jn9
A212
A2
West
Wickham
A2
A2
Bickley
Hayes
A232
Coombe
Lane
22
A1
Jn1a
Albany
Park
Sidcup
2
A22
Chislehurst
A2
Chafford
Hundred
3
Jn30Jn31
A2
02
A102
A1
A3044
02
A 1206
A1206
A2
M25
A13
A1
I L H I
L
L
L
I
I
N
N
12
A406
A117
Ockendon
A 1206
A1206
A215
A 23
14
A2
A24
M25
A406
A117
05
A4
2
11
3
05
A12
A215
0
A218
Jn9
Jn8
Bookham
G
A1
A12
A12
0
20
A1
22
A218
A2
New
Eltham
2
A23
Lebanon
Road
South
Croydon
Sanderstead
Woodmansterne
Chipstead
Tadworth
Effingham
Juction
c2c
Basildon,
Southend and
Shoeburyness
Jn30
Bexley
A2
Reedham
Coulsdon
Smitham (Coulsdon Town)South
A2
Tattenham
Epsom
Corner
Downs
Reedham
Woodmansterne
A2
M2
Bookham
N
3
2
0
A 23
A3
0
Leatherhead
Cobham &
Stoke D’Abernon
Effingham
Juction
West
Horndon
Ockendon
and Chatham
A2
0
West
Wickham
N
D O
Y
O
C R
N
O
Y D
O
C R
Bexley
Mottingham
A2
Bromley South
Eden Park
Addiscombe
Lloyd
A2
12
East
Park
South Sandilands
Croydon
Croydon
Church George
Street Street
A2022
40
5
c2c
Basildon,
Southend and
Shoeburyness
A127
Jn29
E
A12
A11
20
0
22
Ashtead
Wellesley
Road
Centrale
Reeves
Corner
Waddon
Smitham (Coulsdon Town)
40
Jn9
South West Trains
Guildford
A245
M2
2
11
A1
G
A123
6
2
A1
22
A3
Cobham &
Stoke D’Abernon
E
I D
0
A4
A11
6
7
A105
0
5
A3
22
17
A2
A24
22
A20
Banstead
2
5
Epsom
Ashtead
Church George
West
Street Street
Croydon
A2022
Grove Park
A2
A207
Albany
Park
Sidcup
5
A20
Shortlands
A214 A232
Arena
232
A
Woodside
Blackhorse Lane
Lebanon
Road
Waddon
Carshalton
Beeches
022
Addiscombe
Norwood
Wellesley
Road
Centrale
Reeves
Corner
Selhurst East
Junction
Croydon Sandilands
A232
0
New
Eltham
Eden Park
Woodside
Harrington
RoadBlackhorse LaneElmers End
Wallington
Sutton
Bellingham
I
Mottingham
Eltham
A2
Lee
Beckenham Hill
Lower
Sydenham
Anerley
Beckenham
Norwood
Clock
Avenue
Junction
Birkbeck
A214
Junction
House
Arena Road
Selhurst
Therapia
LanePark
Wandle
Ampere Way
Penge
East
H
C
A2
Elmstead
Woods
Grove Park
Sundridge
Kent
New
Penge
Park
Sydenham
House Beckenham
Ravensbourne
West
Beckenham Hill
Lower
Crystal
Beckenham Road
Sydenham
Bromley North
Anerley
Palace
Penge
Elmstead
Beckenham
East
Chislehurst
Woods
A2
Clock
Avenue
Shortlands
Junction
2
Birkbeck
Road
Sundridge 2
House
Kent
Penge House New
Park
Ravensbourne
Beckenham
West
Bickley
Bromley
South
Harrington
Beckenham Elmers
Road
End
Road
Bromley North
Thornton
West
Heath
Croydon
Mitcham
Ampere Way
Common
A232
Sutton
CarshaltonCarshalton
Beeches
BansteadBelmont
Epsom
Downs
15
Wandle Park
Cheam
Ewell
East
A2
Beddington Lane
Waddon Marsh
Hackbridge
N Hackbridge Waddon Marsh
S U T T O Wallington
Belmont
Ewell
East
02
A245
M2
40
Ewell
West
A2
5
Mitcham
Junction
Carshalton
A232
Gipsy
Hill
A214
Norbury Thornton
Heath
Mitcham
Common
Sydenham
A205
Forest
Hill
Hither
Green
W
N
H
C
I
W
N
E
E
E
R
G
A
E
R
G
Kidbrooke
205
A20
CatfordBellingham
Bridge
Catford
Crystal
Palace
Sydenham
Hill
15
Streatham
Common
17
A24
West
Sutton
Cheam
Streatham
Crofton
Catford
Park
A205
WestGipsy
Dulwich
Hill
A2
Catford
Bridge
Ladywell
Honor
Forest
Hill Oak Park
Sydenham
Hill
Tulse
Hill
A214
A232
0
A24
07
A3
A2
5
R E
Y
N E W H A
M
N E W H A
M
North
West Dulwich
Dulwich
Therapia Lane
M2
R
R E
Y
5
Ewell
Stoneleigh
West
2
R
A3
A24
3
4
U
West
Byfleet
South West Trains
Guildford
Oxshott
4
A2
S
U
Sutton Common
04
40
02
44
A2
Woking
M25
S
A2
A2
West
Sutton
A205
Herne Tulse
Hill Hill
Streatham
West
Common
Norwood
Norbury
MitchamBeddington Lane
Eastfields
S U T T O N
Sutton
Common
St. Helier
3
Worcester
Malden
Park
Manor
Stoneleigh
Epsom
EPSOM
AND
EWELL
A3
Byfleet &
New Haw
Morden South
04
3
Oxshott
07
A3
A2
A2
5
Worcester
Motspur
Park
Park
Malden
Manor
Mitcham
Junction
Belgrave Walk
Mitcham
0
A24
M25
A3
A24
EPSOM
AND
EWELL
Streatham
HillStreatham
A214
Phipps Bridge
Morden
South Merton
St. Helier
A2
Chessington
South
Weybridge
West
Byfleet
19
M E R T O N
A298
04
ClaygateA3
07
A3
Morden Road
Park
WimbledonMorden South
Motspur
Chase
Park
A2
Chessington
South
Chessington North
19
A3
A3
A3
Tooting
Broadway
Tooting Bec
Colliers
Tooting
A217
Wood
Haydons
Road
South Wimbledon
Tooting
A219
Park
New
Malden
Chessington North
A3
Tolworth
Hinchley
Claygate
Wood
A244
Walton-onThames
A3
Byfleet &
New Haw
A100
A10
03
A5
5
Weybridge
A127
Jn29
G
A1
23
Harold Wood
R
A1199
N
A5
64
I
A127
E
2
A1
A312
R
5
A 1112
R
G
A123
0
40
A1 06
A4
B
I D
D
06
Gidea
Park
18
A1
A118
Seven
Kings
2
A1
V
2
A1
R
E
2
A1
Romford
A118
A128
E
A
A 1112
B
0
40
A1
A12
25
M
V
D
R
A4
Wanstead
Park
Harold Wood
18
A1
Newbury Park
Barkingside
A12
Brentwood
Jn28
2
A1
2
Hainault
Barkingside
A12
Fairlop
Weald
2
Country
A1
Park
A128
A
11
Redbridge Gants
Hill
25
M
M25
X
112
E
M25
E
112
A1
R
06
A12
Wanstead
H
Fairlop
A1
Brentwood
Jn28
Hainault
Grange
Hill
A4
Street
Walthamstow
Queens Road Snaresbrook
Leytonstone
A
A5
12
X
S
A1
0
0
A406
South
Woodford
Leyton
Wood Road
Midland
Walthamstow
Central
Weald
Country
Park
H
A1
Hainault Forest
11
Country Park 2
Chigwell
02
A4
E
S
E
S
21
01
BU B
CKUC
IN K I
G HN G
A MH A
S HMS
IR HI
E R
S
A1
A1
Woodford
EppingSouth
Forest
Woodford
Wood
Street
Walthamstow
Queens Road Snaresbrook
3
A50
St. James Street
104
East
Dulwich
West
NorwoodA205
Balham
A214
Haydons
Earlsfield
Road
A219
43
M2
Addlestone
M3
Berrylands
Norbiton
KINGSTON
A3 U P O
N
Tolworth
THAMES
Thames
Ditton
Hinchley
Wood
Esher
07
Hersham
8
A23
A
Hill
Morden Road
Broadway
M E R T O N
Colliers
Wood Phipps Bridge Tooting Mitcham
A298
Eastfields
Dundonald Road
Morden
Belgrave Walk
South Merton
Raynes
South Wimbledon
Merton
Mitcham
0
A2
A244
3
L A M B E T H
Clapham
South
Streatham
Common
A217
Merton
Park
Wimbledon
Chase
Berrylands
Surbiton
South West Trains
Aldershot, Basingstoke,
Salisbury, Exeter, Esher
Southampton,
Bournemouth,Poole,
Weymouth, Guildford,
Havant, Portsmouth
(for Isle of Wight),
Chertsey and Staines
Hersham
Walton-onThames
A3
Addlestone
Chertsey
A3
M3
04
20
43G S T O N
KIN
UPON
THAMES
Surbiton
0
A24
5
M3
A3
Hampton
Thames
Hampton
Ditton Court
Court
05
Dundonald Road
Raynes
Park
Wimbledon
New
Malden
08
South West Trains
Aldershot, Basingstoke,
Salisbury, Exeter,
Southampton,
Bournemouth,Poole,
Weymouth, Guildford,
Havant, Portsmouth
(for Isle of Wight),
Chertsey and Staines
Shepperton
Chertsey
M2
Virginia
Waters
A3
A2
Kingston
0
M3
Court
A3050
Wimbledon
Common
A23
Norbiton
A24
Virginia
Waters
South West Trains
Ascot, Bracknell,
Reading and Weybridge
Hampton
Wick
Hampton
Bushy Park
Hampton Court
Hampton
A308
Wimbledon
8
Kingston
7
Kempton Park
Sunbury
3
8
0
A3
A2
Wandsworth
Tooting
Bec
Wimbledon Park
A30
A3050
M
Shepperton
Upper
Halliford
Bushy Park
Teddington
2
South West Trains
Ascot, Bracknell,
Reading and Weybridge
A308
Hampton
Wick
Fulwell
Hampton
1
A3
0
A3
7
12
Upper
Halliford
A30
A3
Teddington
Strawberry Hill
Sunbury
3
M
A308
Wimbledon
Common
Brixton
Clapham Common
Balham
Common
Earlsfield
Wimbledon Park
Southfields
A3
Richmond
Park
THAMES
A313
3
W A N D S W O R T H
Twickenham
UPON
12
A308
Ashford
Clapham High Street
Wandsworth
Common
Clapham
Southfields
A
East
Putney
Richmond
Park
St. Margarets
Strawberry Hill
A3
A30
Staines
Egham
Guildford, Havant,
Portsmouth and
Isle of Wight
Roding
Valley
A406
Highams
Park
Grange
Hill
A5
1
UPON
Kempton Park
Woking
Shenfield
National Express East Anglia
Southend, Chelmsford, Colchester,
Ipswich and Norwich
Hainault Forest
Country Park
Chigwell
Woodford
9
00
A1
A105
A1
1
es
Hounslow
RICHMOND
12
TWhitton
HAMES
0
A3
0
Blackhorse
Road
South
Newington
Tottenham
A4
am
Jn13
Putney
Sheen
Twickenham
A313
RICHM
O NFulwell
D
Staines
0
5
A31
A3
Feltham
Wraysbury
Egham
9
Th
Ashford
A21
er
Jn13
9
12
A3
R iv
Heathrow
Terminal 4
A30
A21
es
Hatton
Cross
Jn14
W
Whitton
Feltham
Wraysbury
A3
3
A50
Roding
Valley
Buckhurst
Hill
6
03
A4
1
A4
am
A3
Sunnymeads
O
L
S
W
O
L
S
A408
Th
Datchet
Debden
Shenfield
Epping
Forest
Walthamstow
Central
Tottenham
Hale
Stoke
Highams
Park
A1009
7
A1
02
A5
A5
A5
12
Datchet
er
Windsor &
Eton Central
A3
R iv
Windsor &
Eton Central
A406
Northumberland
Road
A10
Harringay
Green Manor House
Lanes
01
Harringay
National Express East Anglia
Southend, Chelmsford, Colchester,
Ipswich and Norwich
Buckhurst
Hill
69
A10
A100
03
A5
25
Chingford
9 01
00A1
A1
5
A12
A1
Hampstead
Heath
10
M
H A
A M
L T E S TT H
T
W A O R A L R E S
W
F
F O
0
A1
03
21
5
A10
5
A598
I N G E Y
H A R
Sisters
Hornsey
A1
St. James Street
Stamford
SevenHill
M
Chingford
A1009
South
Tottenham
Turnpike Lane
Debden
Loughton
Loughton
Tottenham
Park
Hale
Grove
0
Green
08
A1Lanes
25
69
Blackhorse
Turnpike Lane
Crouch
Hill
Highgate
A1
A598
1
A4
Golders
Green
Bruce
Grove
White Hart
Lane
A1
5
A10
0
A1
A1
Alexandra
Palace Harringay
A504
4
A5
0
08
03
A1
A1
A50
East Finchley
140
A4
4
A40
0
09
A4
37
& Eton
Riverside
Windsor
& Eton
Riverside
D
G
L I N
E A
G
L I N
E A
A408
M4
OR
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N
I
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D
W
A N R EA
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S
E
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M IAN N D
W
AD
A
HE
N
IDE
MA
G
N
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A4
Slough
S LO
UG
H
Windsor
A41
0
M4
A4
Crossrail
Maidenhead
First Great Western
Reading, Oxford,
Banbury, Worcester,
and Bedwyn
Slough
S LO
UG
H
M1
4
A40
09
Burnham
G
37
First Great Western
Taplow
Reading, Oxford,
Banbury, Worcester,
and Bedwyn
06
A4
B R E
N T
N
O
D
A4
Crossrail
Maidenhead
02
Brent
A504
Cross
Hendon
Central
B R E
N T
A4
0
02
A4
A4
A41
A4006
Kingsbury
Bowes
Alexandra
Park
Palace
East Finchley
Finchley
Central
Wood Green
E
M
Epping
Forest
A1
Angel Northumberland
A406
Road Park
G ESevenY
I N Green
H A RHornsey Wood
Sisters
Bruce
Harringay
A504
A50
A5
A1
White Hart
Lane
Silver
Street
Green
Bounds
Green
New
Southgate
A1003
4
Hendon
Central
Hendon
140
A4
A409
0
0
02
A355
Burnham
Taplow
Bowes
A406
Epping
Forest
A10
Angel
Road
Edmonton
Green
Arnos Grove ParkPalmers
M1
04
18
04
Rayners
Lane
A4
A355
0
A406
E
Figure 2-2. Geographic
map of London rail services
(source TfL).
Jn27
Theydon Bois
South
Finsbury Park
Chadwell
02
Archway
Leytonstone2
Kenton Kenton
Hendon
Newbury ParkGoodmayes
Northwick
A1
Heath
Crouch
Rectory
Leyton
I S L I N G T O N Stamford
High Road
Wembley Park
Bridge
A124 Romford
C A M DHighgate
E N Tufnell
Clapton Hackney
Upper
07
Road
Hill
Ilford
Hill
Midland Road
Preston
A1083
A118
South A400Park
WansteadRedbridge Gants
Brent
A4
Gospel Park
Marsh
North
Holl oway
A1083
Hornchurch
A124
Wanstead
Arsenal
03
Cricklewood
Road
Flats
Harrow 5
Cross
Hampstead Oak
Hill
A5
BARKING
Wembley
A12
Sudbury Hill
Emerson
Leyton
Manor
104
Eastbrookend
HarrowWanstead
A
West
Denham
Heath
01
Holl
oway
Neasden
Manor House
HACKNEY
Stoke
A118
Golders Hampstead
Dollis
Harrow
Park
06
Park
Wembley
Seven
Country Elm Park
South
18
on-the-Hill
Park
A4
Harrow
A
N
D
1
Road
Leytonstone
Sudbury
&
Wanstead
A
Newington Hackney
Dalston
Willesden
Northolt Park
Drayton Park
Park
Green West Finchley Road Hampstead
Stadium
Hill
Kings
Park
Eastcote
Kentish
Upminster
South
Ruislip
Finsbury
A40
Ickenham
A124
Chadwell
Woodgrange
Park
Archway
Harrow Road
Park
A124
Heath
&
Frognal
Homerton
Leytonstone
Kingsland
D A G E Heath
N H A M Dagenham
Green
Upminster
Downs
Town
Hampstead
Forest Gate
Canonbury
Kenton
Goodmayes
Sudbury Hill
Ruislip
Stratford
Kentish
Rectory
Ruislip
24
Caledonian
ISLINGTON
Wembley Park
Bridge
Wembley
East A124
Kilburn
Tufnell
International
Dagenham
A1
C ABelsize
M DPark
E N Town
Clapton Hackney
MarylandHigh Road
Upper
07
Gardens
Road
Highbury & Islington
Ilford
Road
Manor
Jn16
A1083
South A400
Wanstead
A4
Ruislip
Gospel Park
Heathway
North Central
West Ruislip
Holl oway
A1083
Dalston
Hackney MarshHackney
Hornchurch
Chalk Farm West
A124
Arsenal
Camden
Finchley Hampstead
Cricklewood
Flats
Harrow 5
12
Sudbury
Becontree
Junction
Oak
A118
BARKING
Road
Wembley
Stonebridge Park
A3
Barking
Road
Sudbury Hill
Central
Essex Road
Eastbrookend
Wick Stratford Leyton
Swiss Cottage
Wanstead Manor
Caledonian
Heath
Holl oway
East Ham
Town
Neasden Dollis Brondesbury
Hampstead
H
A
C
K
N
E
Y
Elm
Park
Upney
Hillingdon
Brondesbury
Harrow
Park
Wembley
Road
&
8
London
Country
South
1
Park
AND
Camden
RoadBarnsbury Drayton Park
Victoria
Sudbury &
A404
A1
Kilburn
Dalston Fields
WillesdenPark
Northolt Park
Stadium
Hill
Town
Hackney
Park
Stratford
Kentish
Ruislip A40 Northolt
A40
HighWest
Road Finchley Road
Ickenham
Woodgrange Park
Harrow Road
A124
& FrognalSouth
High Street
HomertonPark
Hornchurch
Kingsland
D A G E N H A M Dagenham
Upton Park
Kensal
Green
Harlesden
Downs
Town
Hampstead
Forest Gate
Canonbury
Haggerston
Hampstead
Sudbury
Hill
Ruislip
Stratford
Kentish
Mornington
Alperton
24
Country
Caledonian
Queen’s
Uxbridge
Wembley
East
Kilburn
International
Dagenham
Crescent
St. John’s Wood
A1
Belsize Park
A1
Willesden Rise
Maryland Plaistow
King’s Cross
Gardens
Town
Highbury & Islington
Road
Jn16
Angel
Park
St.
25
Park
Greenford
Central
Pudding
Cambridge
Kilburn
M2
Heathway
Junction
Pancras
Dalston
Abbey
Hackney
Chalk Farm West
Dagenham
Mill Lane
5
12
Camden
Park Finchley
International
12
A13
Sudbury
A130
Becontree
Junction
Bethnal Hackney
A1 18
Heath
Hoxton
A1
West Road
A3
Road
Stonebridge Park
A3
Barking
Road
6
Essex Road
Dock
Maida Swiss Cottage Regent’s
12
Perivale
Wick Stratford
Caledonian
Brondesbury
124
8 Central
EastAHam
Town
Green
Vale
Ham
A4020
Upney
Hillingdon
Brondesbury
Park
A120
Road &
Mile
London
South
Kensal Green
Park
Camden
Euston
King’s Cross
Bow
Church
Victoria
End
A404
Kilburn
Barnsbury
Bethnal Green
Northolt
GreatTown
St. Pancras
Fields
Greenford
Old Street
Stratford
North
High Road
A40
Park
W
E
S
T
M
I
N
S
T
E
R
South
Baker Portland
High
Street
Hornchurch
Upton
Park
Hanger Lane
Kensal
Shoreditch
Harlesden
Bow Road
Haggerston
Street Street
Warwick
Avenue
Acton
Hampstead
High
Russell
Marylebone
Mornington Euston
Alperton
Queen’s
Uxbridge
Rainham Country A
Street
A40
Farringdon
Square
Crescent
Square
St. John’s Wood
A1
Park Royal
Willesden Rise
Edgware
BromleyKing’s Cross
Devons
Stepney Green
13
Angel
Park
St.
Moorgate
Plaistow
25
Road
Park
Greenford
06
Road Pudding
Goodge Street
Cambridge Whitechapel
Kilburn
Wormwood
M2
Junction
by-Bow Abbey Star Lane A13
Regent’s Warren Pancras
Royal Oak
Liverpool
Dagenham
Mill Lane
5
12
Park
Street
InternationalTottenham
A13
Park
Beckton
A130
West
Road
Bethnal
A1
Street Heath
Scrubs
Hoxton
A3
North
Court Road
Edgware
Holborn
6
Regent’s Oxford
Aldgate
4
A402
Dock
Maida
T OLangdon
W EParkR West
12
Barbican
Perivale
A13
Ealing
Road
A12
Bond Circus
0
Westbourne
Acton
Green
208 East
Castle
East Acton
Vale
Ham
A4020
Ealing
Park
St. Paul’s
A1
Chancery
Street
Mile
SouthBar Park
Park
Aldgate
Bayswater
Kensal Green
Euston
L Church
ETS
Broadway
King’s Cross Lane
Canning Town
Bank
R iv
End H A MBow
A4
Paddington
Ladbroke
City Old Street
Fenchurch St.Bethnal Green
Great
St.
Pancras
A1
Greenford
e
Covent
Leicester
North
r
Beckton
08
Mansion
Grove
Limehouse
T ha
WESTMINSTER
Thameslink
3
Marble
Arch Portland
Tower
Baker
Royal Custom House Prince
Gallions
Garden
Square
Shadwell
Park
Shoreditch
Drayton West Hanger Lane
Royal
m es
Poplar
All Saints
House
Bow Road
Hill
Temple
for ExCeL
Regent
Street Street
Victoria
Reach
Warwick Avenue
Acton
Latimer
Lancaster
High
Acton
Albert
A40 White City
Russell
Westferry
Marylebone
Euston
Charing
Green Ealing
Road
East
Rainham
Street
A40 Main Line
Blackwall
Farringdon
Square
Tower
Square
Cyprus
Queensway
Park Royal
Edgware Gate
BromleyA1
Cross
Blackfriars
Devons
Cannon Moorgate
A1203Stepney Green
Gateway
Notting
West India Quay India
30
Road
Acton WormwoodWood Lane
Piccadilly
Road
Goodge
Street
Hyde Regent’s
Monument
Warren
Street
London
Embankment
Star Lane
6
by-Bow
Hill Gate
Whitechapel
Royal
Oak
Green ParkStreet
Circus
Liverpool
A13
Tottenham
West
City Airport
Ealing
Canary Wharf
Central Scrubs
Park
Beckton
Park
West
A1020
Shepherd’s Bush
Street
Holland
North
Court Road
Kensington
Edgware
West
A2016
Holborn Waterloo East
Aldgate
A402
Oxford
T
O
W
E
R
Barbican
Market
London Bridge
Drayton
Park
A13
Hanwell
Ealing
Wapping
Common
Road
Silvertown
Westminster
Bond
A4020
Heron
0
Westbourne
Acton
East
Langdon
Park
King
Circus
Castle Bar Park
East Acton
Shepherd’s
Gardens
Ealing
St. Paul’s
A2016
Chancery
A2
Street
Southall
Quays
Park
George V
A315
Aldgate
Bayswater
Bush
Pontoon
H
A
M
L
E
T
S
Broadway
00
Canning
Town
Bank
Lane
South
Acton
R
Southwark
A4
High
Street
Hyde
Park
iv e
Hayes &
Paddington Knightsbridge
Goldhawk
Ladbroke
City
Fenchurch St.
Rotherhithe
A1
North Custom HouseDock PrinceThames
Covent
Leicester
South
r T
Beckton
08
South
Kensington
Waterloo Thameslink
Mansion
Road
Grove Kensington
Limehouse
3
Marble Arch Corner
Tower
Royal
h
Gallions
Garden
West
Square
a
Harlington
Shadwell
Park
Quay
Drayton
Royal
m
Poplar
Borough
Turnham
All
Saints
(Olympia)
Acton
es
House
Iver
Hill
Temple
for ExCeL
RegentFlood
Greenwich
Abbey
Victoria
St. James’s Lambeth
Reach
Ealing
North
Latimer
Lancaster 4
Acton
Albert
A40 White City
Canada
Water
Westferry
Park Charing
Barrier
Green Ealing
Green
Road
East
Gate A
Blackwall
Town Main Line
Wood
Tower Bermondsey
Cyprus
Queensway
Langley
Gloucester
Cross
Northfields
Wood Lane
Woolwich
Blackfriars
Cannon
Crossharbour
A1203
Sloane
Gateway
Notting
West India Quay India
Acton
Road
Piccadilly
Hyde Square
StreetElephant Monument
Hill Gate
Earl’s
& Castle
Belvedere
Green Park
Victoria Embankment
06London
Circus
Hammersmith
West
Ealing Chiswick
CanaryQuays
Wharf
Central
Court
Surrey
A2 City Airport
Park
A1020
Shepherd’s
Bush
Holland
Boston
Manor
Kensington
West
A2016
Ravenscourt
Waterloo East
Woolwich Woolwich
Barons
Stamford
Park
Mudchute
Market
London Bridge
Drayton
Park
Hanwell
Wapping
South
Common
Westminster
Park
A4020
Heron
Court
Island Gardens Silvertown
King
Plumstead
Shepherd’s
Gardens
016
Dockyard
A2
Westcombe
Kensington
A
Southall
Quays
Brook
George
V
M4
A315
2
Bush
Pontoon
Arsenal
00
Kennington
A2
A206
South Acton
Southwark
K Street
E N S I NKnightsbridge
G T O N Hyde Park
High
Jn15
Hayes &
Erith
Goldhawk
Park Dock
Rotherhithe
00
Purfleet
North
West
SouthM4
Charlton
South
South Bermondsey
Kensington
Waterloo
Corner
Kensington
Pimlico
Gunnersbury
Road
Thames
West
Kew
Harlington
AND
Quay
Borough
Turnham
(Olympia)
Acton
Iver
Flood
Greenwich
Kensington
Brompton
Cutty Sark
Abbey
St. James’s Lambeth North
Ealing
A4
es
M4
Canada Water for Maritime Greenwich
Bridge
Park
Barrier
am
Green
M4
C H E L S AE4 A
A3044
Vauxhall
Town
Wood
Th
Langley
Gloucester
Northfields
Bermondsey
Woolwich
r Battersea
Brentford
Crossharbour
Chiswick
Maze Hill
eSloane
A2
Road
Fulham
Deptford
R i v Square
Earl’s
Elephant & Castle
Belvedere
H ABroa dway
Victoria
06
Chiswick
Hammersmith
Oval
2
Park
A4
Court
Surrey
Quays
A
Greenwich
Battersea
Boston Manor
Woolwich Woolwich
BaronsM
Stamford Ravenscourt
Park
5
Mudchute
Slade
Osterley
A4
ME
A207
South
Park
Queens Road
Greenwich
Court
Park
Island Gardens
Park
A31
Dockyard Arsenal Plumstead
Westcombe
Kensington
A4
Brook
Deptford
M4
A202
Green
Syon Lane
Peckham
Kennington
Queenstown
A2New
A206
Parsons Green S M West
Bridge
Jn15
Erith
Park
New
Kew
00
Purfleet
I TK E N S I N G T O N
M4
Charlton
South Bermondsey
Hounslow
Pimlico
Gunnersbury
Road
West
Cross
Imperial
Kew
Peckham
Stockwell
H
AND
A2
Barnes
Denmark
Cross
Gardens
Syon
Kensington
Brompton
Cutty Sark
A207
A4
es
East
M4
F U A N Wharf
Bridge
Rye
for Maritime Greenwich Elverson Road
am
Bridge
Hill
M4
Loughborough
C
H
E
L
S
E
A
A3044
Gate
h
Vauxhall
Royal
Park
D
Heathrow
T
Bexleyheath
LH
Welling
r
Brentford
Chiswick
Maze Hill
St. Deptford
Johns
A2
Isleworth
Junction
Lewisham
Botanic
i v e Battersea
Barnehurst
Fulham
Broa dway
Terminals
Putney
Bridge
A
R
H
Clapham
Oval
M
Heathrow
Clapham Junction
Brixton
Wandsworth
Nunhead
A4
AM
Gardens
Greenwich
Battersea Park
Hounslow Hounslow
1,
2,
3
North
Blackheath
Barnes
5
Terminal
5
Slade
Road
Osterley
A4
ME
A207
Queens Road
Greenwich
Park
Mortlake
Park
A31
West
Brockley
A4
Deptford
Central
A202
Green
Syon LaneRichmond
SM
Falconwood
Peckham
North
0
Town
Queenstown
A205
Clapham High Street
Putney
New
Parsons
Green Wandsworth
Bridge
A3
New
Kew
Kidbrooke
Brixton
IT
East
Sheen
A20
Hounslow
Road
Jn1a
A2
Cross
A207
Peckham
H
Clapham Stockwell
A2
Sunnymeads
Barnes
Denmark
Cross
Gardens
Syon
A NImperial
Hatton
Dulwich
A207
Crofton
Jn14
5
East
F
Elverson Road
Clapham Common
Rye
Wharf
Common
A31
Bridge
Hill
Loughborough
Hither
Gate
Hounslow
Royal
Park
D
Heathrow
Park
East Putney U L
Cross
Eltham
Bexleyheath
A2
Dartford
Welling
St.
Johns
H
Isleworth
05
Lewisham Green
Botanic
Barnehurst
Ladywell
LClapham
A M B EBrixton
T Junction
H
Crayford
Terminals
Putney Bridge
A
North
Heathrow
Wandsworth
Nunhead
W A N D S W O MR T HClapham Junction
Gardens
St. Margarets
Herne
Hounslow Hounslow
1, 2,Heathrow
3
North
Honor
Lee
Dulwich
Blackheath
Barnes
Terminal 5
Road
Terminal 4
Clapham South
Mortlake
Hill
West
Southeastern
Richmond
Brockley
Oak Park
Central
Falconwood
North
Gravesend
0
Wandsworth Town
A205
Ruislip
Ruislip Manor
West Ruislip
Denham
Golf Club
M4
A1
Silver
Street
BoundsA111
Green
New
Southgate
Jn26
10
Ponders
End
Bush Hill
Edmonton
Park
Green
Palmers
Green
Arnos Grove
Southgate
9
Ponders
Brimsdown
End
5
M2
A121
Southbury
Winchmore
Hill
A10
A1003
Enfield
Bush
Hill
Town
ParkA110
A111
11
A5
A4006
6
9
9
A504
Kingsbury
Colindale
A400
A10
Lee Valley
Country
Park
Southbury
E N F I E L D
Grange
Southgate
Park
Oakleigh Park
10
A5
A5
0
A414
A4
A4
A4
A110
Finchley
West Finchley
Central
Mill Hill East
Burnt Oak
A400
Kenton
Queensbury
Preston
Road
HarrowWest
North
Harrow on-the-Hill
Harrow
E T
Totteridge &
R N
Woodside
Park
Whetstone
B A
West Finchley
E T
Mill Hill East N
R Woodside Park
B A
Colindale
6
A409
0
04
H
0
A414
18
A4
Pinner
Rayners
Lane
A1
Mill Hill
Broadway
Burnt Oak
Edgware
Harrow & Canons Park
Wealdstone
Queensbury
Headstone
Lane
A1
0
A4
2
A
Eastcote
MA44
00
14
04
04
Northwood
Hills
A410
O W
North Harrow R
R Northwick
Harrow &
A
Wealdstone
Park
H
4
40
W
R O
R
A
A4
Pinner
A4
1
A4
Gerrards
Cross
A4
5
Hatch
End
A1
A41
10
Canons A4
Park
Stanmore
A5
Moat Mount
Open Space
Mill Hill
Broadway
Edgware
Oakleigh Park
Totteridge New
&
Barnet
Whetstone
9
10
Oakwood
A1
0
5
12
04
04
A4
Northwood
Denham
A410
Headstone
Lane
A4
2
A40
A41
M1
Stanmore
Bentley Priory
Open Space
Northwood
Hills
Denham
Golf Club
14
12
A4
Northwood
Gerrards
Cross
Seer
Green
A4
A4
4
A40
Carpenders Hatch
Park
End
1
A4
Seer
Green
Beaconsfield
A411
Elstree
Open Space
A410
Bentley Priory
Open Space
Moor
Park
Moat Mount
Open Space
A1
M1
1
A41
Carpenders
Bushey
Park
Cockfosters
High
Barnet
A411
ElstreeElstree & Borehamwood
Open Space
Enfield
Town A110
Enfield
Gordon Hill
Chase
Grange
Park
0
A11
Enfield
Chase
Winchmore
Hill
Oakwood
11
A41
Aldenham
Park
A411
Moor
Park
4
A40
3
A41
Rickmansworth
A411
1
1
0
Lee Valley
Country
Park
Enfield
Brimsdown
Lock
Turkey
Street
E N F I E L D
5
A11
Trent Park
Country
Park
A110
A1
A4
A5
3
A41
Jn17
New
Barnet
1
Bushey
00
Cockfosters
M
12
A4
Rickmansworth Croxley
Elstree & Borehamwood
A1
Trent Park
Country
Park
1
Watford
High Street Watford
Junction
Enfield Waltham
Lock Cross
Jn27
Theydon Bois
A121
A121
Turkey
Street
Jn25
High
Barnet
A411
Aldenham
Park
A411
4
Wrotham
Park
1
Watford
High Street
A40
Jn17
1
A4
Watford
Beaconsfield
Chiltern Railways
High Wycombe, Aylesbury,
Banbury and Birmingham
2
1
Cassiobury
A4
Park
Croxley
Jn18
Chorleywood
M25
Crews Hill
Epping
5
M2
Jn26
Gordon Hill
A11
4
5
1
A40
00
Hadley
Wood
081
A1
11
A4
Jn18
Chorleywood
M
Watford
North
A5
Cassiobury
Park
Grove
Watford
Park
5
M2
4
Jn23
A1
M25
Jn24
081
A1
Watford
Junction
Jn19
A40
Hadley
Wood
Wrotham
Park
11
Chalfont
& Latimer
A121
Waltham
Cross
Jn25Theobalds
Grove
Watford
North
A41
Grove
Park
A4
Amersham
Radlett
Garston
M2
Epping
Cheshunt
M25
Crews Hill
Jn24
A11
Jn19
5
4
A1
Jn20
A40
M25
A5
M1
A41
Potters Bar
Jn23
H IR E
T F O R D S
H E R
Radlett
Garston
Chiltern Railways
Aylesbury
Theobalds
Grove
Cuffley
A1
01
0
Bricket Wood
5
A10
00
Jn20
Chiltern Railways
Aylesbury
M2
National Express East Anglia
Broxbourne, Hertford East,
Harlow, Bishops Stortford,
Stansted Airport and Cambridge
A1(M
M1
Jn21a
Cheshunt
First Capital Connect
Hertford North
and Stevenage
First Capital Connect
Welwyn Garden City,
Stevenage, Letchworth,
Cambridge, Kings Lynn,
Huntingdon and
Potters Bar
Peterborough
H IR E
T F O R D S
H E R
Jn22
Jn21
Kings Langley
Chalfont
& Latimer
A5
Kings Langley
M25
Cuffley
First Capital Connect
St. Albans, Luton Airport,
Luton and Bedford
London Midland
St. Albans Abbey
Chesham
Amersham
5
Jn21a
Bricket Wood
How Wood
A10
Jn21
London Midland
Milton Keynes
and Northampton
National Express East Anglia
Broxbourne, Hertford East,
Harlow, Bishops Stortford,
Stansted Airport and Cambridge
A1
01
0
Jn22
M25
Southern
Milton Keynes
First Capital Connect
Hertford North
and Stevenage
First Capital Connect
Welwyn Garden City,
Stevenage, Letchworth,
Cambridge, Kings Lynn,
Huntingdon and
Peterborough
M2
A 10
How Wood
Chesham
Chiltern Railways
High Wycombe, Aylesbury,
Banbury and Birmingham
First Capital Connect
St. Albans, Luton Airport,
Luton and Bedford
London Midland
St. Albans Abbey
A 10
London Midland
Milton Keynes
and Northampton
A1(M
Southern
Milton Keynes
Sevenoaks
Southeastern
Tonbridge, Hastings,
Ashford, Canterbury,
Folkestone and Dover
© Transport for London
Kempton Park
A
A308
M
A3050
South West Trains
Ascot, Bracknell,
Reading and Weybridge
20
2
Green Park
Hyde Park
Kensington
Gardens
S
Victoria
A10
RE
17
A3
Cannon
Street
Y
Aldgate
Fenchurch
Street
Tower
Hill
Shadwell
Tower
Gateway
Monument
5
A1203
Stoke D’Abernon
00
Rotherhithe
Borough
Canada Water
01
Leatherhead
Bermondsey
Effingham
Juction
Elephant & Castle
Bookham
South West Trains
Guildford
A2
Sloane
Square
02
Guildford, Havant,
Portsmouth and
Isle of Wight
Kennington
A2
Pimlico
Figure 2-3. Underground, National Rail, and DLR services in Central London (see Figure 2-2
for legend; source: TfL).
Oyster cards can store prepaid weekly, monthly, or annual Travelcards
valid for unlimited use on various modes within specified travel zones,
and can also store monetary credit, which can alternatively be used to pay
for each trip individually (referred to as pay as you go). All Oyster users are
required to tap their cards when boarding buses or when entering or exiting station gates, but some stations are ungated. Travelcard holders are
not required to tap at ungated stations,3 but Oyster readers are provided
for pay-as-you-go users, who are required to tap their cards in order to
deduct the correct fare.
Oyster data is retained by TfL for eight weeks before being archived4
and the complete population of Oyster data for one week of every month
3 Travel beyond the zones (or time periods) covered by a Travelcard requires the customer to
pay an additional fare (the difference in fare between travel covered by the card and the trip
taken by the customer) using pay as you go.
4 For privacy, identifying information is removed prior to archival.
28
5
Wapping
Cobham &
A2
A201
South West Trains
Guildford
A24
A245
M2
London Bridge
Southwark
A2
Oxshott
07
A3
A2
National Rail
main line connections
A
17
2
A3
A3212
South
Kensington
R
A3036
02
Gloucester
Road
30
A3216
Earl’s
Court
A4
U
Blackfriars
Lambeth North
St. James’s
Park
2
Mansion
House
Waterloo
East
Waterloo
A3
High Street
Kensington
Hyde Park
Corner
Knightsbridge
City
Thameslink
Embankment
5
Bank
Temple
Westminster
Woking
A315
Riv
er
Th
am
es
Piccadilly
Circus
St. Paul’s
1
A1
Aldgate
East
A3
A10
0
Charing
Cross
A24
4
A40
A4
A4
Queensway
Covent Garden
Leicester
Square
Marble Arch
Lancaster
Gate
A40
Barbican
West
Byfleet
A3
4
A2
Paddington
Holborn
Liverpool
Street
A3
A4206
Chancery
Lane
Tottenham
Court Road
Oxford
Circus
A5
Bayswater
Bond
Street
Claygate
07
A3
Whitechapel
Moorgate
Byfleet &
New Haw
M25
Edgware
Road
Bethnal
Green
High Street
Farringdon
A
Hinchley
Wood
A244
Shoreditch
Weybridge
01
A52
Russell
Square
1
520
Goodge Street
0
A421
Regent’s
Park
Warren
Street
Walton-onThames
Bethnal
Green
A3
A3
8
Old Street
A1
Edgware
Road
Euston
Square
0
20
A4
19
Royal Oak
Notting
Hill Gate
Great
Portland
Street
Baker
Street
Marylebone
A40
0
A421
1
Warwick
Avenue
1
Cambridge
Hersham
Heath
A120
Hoxton
A50
King’s Cross
St. Pancras
Thames
Ditton
Esher
Angel
Addlestone
A4
A5
4
King’s Cross
Euston
5
20
A40
M3
25
A5
Regent’s
Park
Chertsey
Mornington
Crescent
St.
Pancras
International
St. John’s
Wood
South West Trains
Aldershot, Basingstoke,
Salisbury, Exeter,
Southampton,
Bournemouth,Poole,
Weymouth, Guildford,
Havant, Portsmouth
(for Isle of Wight),
Chertsey and Staines
London
Fields
Central London
Connections
00
A41
A5
Essex Road
2
A1
205
Maida
Vale
M3
Camden Town
M
South
Kilburn Hampstead
High
Road
H
Hampton
Court
Shepperton
Virginia
Waters
0
A3
A244
Upper
Halliford
A3
Bushy Park
Hampton
Sunbury
3
Teddington
A311
A3
M2
5
A308
12
Egham
30
South
Bermondsey
Southern and
South West Trains
Dorking and
Horsham
was made available for this research. Card identifiers are encrypted for
privacy, and data are exported from a TfL database in plain-text format.
Oyster entry and exit data are available for Underground, Overground, and DLR transactions at gated stations (and at ungated stations
for pay-as-you-go transactions), while boarding (but not alighting) information is available for buses and trams.5 The Oyster card was recently
made available for National Rail travel, and its use on the mode has been
steadily increasing (Muhs 2012).
2.2.2
iBus
iBus is TfL’s AVL system, and is installed on the entire fleet of buses. While
several data sets are recorded by the system, this research uses a set containing a record for each stop event: an instance of a vehicle serving (or
passing) a bus stop (TfL 2006, Hardy 2007, Robinson 2010, Continental
Automotive 2011, Hounsell et al 2012).
iBus tracks each vehicle’s location using a combination of GIS, tachometer, speedometer and gyroscope data. The system attempts to record information about the time at which each bus neared a stop, opened its
doors, closed its doors, and pulled away from the stop. If all four data are
recorded internally, it reports the door opening time as the stop arrival
time and the door closing time as the stop departure time. If either door
event is unavailable, the approaching or departing timestamp is used. If
only one of the four events are recorded, the same time is written to both
the arrival and departure fields.
iBus typically records approximately 5 million transactions daily.
Data are provided to MIT researchers through a secure database reporting interface, so that data can be obtained for the same periods for which
Oyster data are collected.
2.2.3
Gateline and ETM
Aggregate counts of passenger activity are provided by rail station gates,
or gatelines, and at bus fareboxes, or electronic ticket machines (ETMs). Rail
5 Modes requiring both entry and exit taps store a record for each. Entry records contain
entry information (as expected), while exit records store both entry and exit information
in order to assess the correct fare.
29
counts are aggregated by date, hour, station, direction (entry versus exit),
and ticket type. ETM data are similarly aggregated by date, hour, and ticket
type, plus the bus’s direction (inbound versus outbound) and its route.
ETM data were available for all bus routes, but gateline data for several
gated National Rail and Overground stations were unavailable (although
these data are recorded and may be available for future analyses).
2.2.4
Spatial Data
In addition to Oyster, iBus, gateline, and ETM data, some of the methods
presented in this thesis require spatial information. A list of bus stops was
acquired from TfL, which includes spatial coordinates. A similar table
of rail stations was also acquired, although it lacked spatial information.
Using each station’s unique identifier, the data were joined to a similarly
identified GIS layer to obtain spatial coordinates.
30
3
Bus Origin and Destination Inference
In order to observe entire passenger journeys using fare-transaction data,
time and location information must be obtained or inferred about the
start and end of each journey stage.1 The zonal fare-pricing structure of
most rail modes in Greater London necessitates fare transactions at both
the start and end of each stage, yielding time and location data for both
the entry and exit stations.2 But London Buses, like many other urban bus
systems, charges flat rather than zonal fares, enabling fare transactions to
be completed at the time of boarding and therefore requiring no alighting
transaction.
Oyster bus records contain information about the time of the transaction and about the vehicle, route, and vehicle-trip number, but information about the alighting location and time must be inferred. Furthermore,
the onboard AFC equipment does not obtain location data from the bus’s
1 The concept of a journey stage is defined in Chapter 4, where it is relevant to the inference
of interchanges. In the context of bus travel, however, a bus journey stage can be considered one cardholder’s ride on a single vehicle, regardless of whether that person made
another bus (or rail) trip immediately before or after it.
2 As discussed in Chapter 2, all London Underground, most London Overground, and many
National Rail stations are gated, requiring transactions for both entries and exits. Ungated
stations, including the remaining Overground and National Rail stations, and most DLR
platforms, require Oyster users to tap their cards only if they are paying a fare using stored
credit. Customers with travel passes valid for the journey stage are not required to tap their
cards at either end.
31
AVL system.3 The boarding location must therefore also be inferred, by
merging the two data sets offline.
This chapter describes the process by which boarding and alighting
locations and alighting times are automatically inferred for the population of Oyster bus transactions for a given day. The resultant data are a
required input to the interchange-inference process, which in turn is an
input to the full-journey matrix estimation process (both of which are described in subsequent chapters). In addition to their applicability to fulljourney analysis, OD-inferred bus data are useful for a number of bus-only
other applications, which are presented in Chapter 7.
3.1
3.1.1
Previous Research
Inferring Bus Origins Using AFC and AVL Data
On bus systems for which AFC and AVL data are available, passenger boarding locations are typically inferred by matching each AFC record to an AVL
record, then using the AVL location as the passenger’s boarding location. In
most cases the records can be matched by first selecting the subset of AVL
records that share the same vehicle, route, or vehicle trip as the AFC record,
and then by selecting from that subset an AVL record that has a timestamp
closely matching that of the fare transaction.
Zhao et al (2007) propose this approach in their study of the Chicago
Transit Authority’s bus and rail system, and Cui (2006) applies the methodology to the city’s bus network. Chicago’s AFC data uniquely identify
specific vehicles, and each AVL record indicates the time at which a bus
opened its doors. By including only those AFC transactions that occur
within five minutes after an AVL event, Cui infers the origins of approximately 90 percent of all bus boardings.
Wang et al (2011) apply a similar approach to five TfL bus routes,
matching AFC transactions to the (temporally) closest AVL record, regard-
3 TfL’s new fare-payment system, currently in development, will record location data with
each fare-transaction record.
32
less of whether it occurs before or after the fare transaction.4 Following
Cui, Wang limits matching to within a five minute range (in this case
either before or after the time of the AFC transaction), yielding a similar
origin-inference rate of 90 percent. Munizaga et al (2011) infer origin locations for 98.5 to 99.9 percent of all bus boardings on Chile’s Transantiago
network, but it is unclear whether a time limit is being applied.
3.1.2
Inferring Bus Destinations Using AFC and AVL Data
The absence of AFC alighting transactions is common to many bus systems.
Lacking this information, previous studies have inferred alighting locations under the assumption that the most likely place for a passenger to
alight a bus is the stop closest to her next bus boarding (or station entry)
location. Barry et al (2002) apply this assumption in their study of New
York’s subway system, and later on the city’s combined bus and subway
network (2009), both of which record fares only upon entry. By making the additional assumption that a rider’s initial origin of the day is a
close approximation of her final daily destination, they are able to infer
destinations for passenger journey stages regardless of a stage’s sequence
within a rider’s daily travel history.
Barry et al (2002) tested these assumptions against a travel-diary survey, showing that the assumptions were valid in 90 percent of the cases
observed. Additionally, Navick and Furth (2002) applied the same assumptions in their study of five Los Angeles bus routes, validating the
assumptions against ride-check data which showed that the assumptions
were valid on four of the five routes studied.5
The two aforementioned destination-inference assumptions have
since been applied in several other studies of transit networks with entryonly AFC data. Zhao et al’s (2007) work on the CTA rail system inferred
destinations for 71 percent of AFC passenger trips, inferring destinations
only when the candidate alighting location is within a 400-meter Euclid4 Multiple event types can trigger the recording of an AVL event in TfL’s system, which can
sometimes lead to ambiguity about whether a bus was arriving at or departing from a stop
(see section 2.2.2 for details).
5 Navick and Furth infer the validity of these assumptions by testing the symmetry of each
route’s boarding profile—the pattern of daily boardings in one direction should match the
pattern of daily alightings in the other. They hypothesize that the lack of symmetry on the
one route that failed their test was due to its sharing of a corridor with other routes.
33
ean distance of the rider’s next origin. Cui (2006) applied a 1,110-meter
rectilinear distance limit to the CTA’s bus network, achieving a destination-inference rate of 67 percent. Trépanier et al (2007) applied similar
logic to the bus network of Gatineau, Québec, using a two-kilometer
Euclidean distance limit and inferring destinations for 66 percent of the
observed transactions.
Wang et al (2011) apply the closest-stop and last-of-day assumptions in
their London study, in which AVL data are used to infer passenger alighting times after the inference of alighting locations. As in the origin-inference process, AVL and AFC data are matched by route and vehicle trip, but
the AVL record is chosen from the resultant subset by matching its bus stop
code to that identified using the two destination-inference assumptions.
Studying five routes and imposing a one-kilometer distance limit around
each route, Wang et al infer destinations for 57 percent of the observed
AFC data. Finally, the inference process is validated against TfL’s Bus Origin–Destination Survey (BODS), which exhibits a similar distribution of
alighting locations for the observed routes.
While most previous studies inferred destinations by using SQL queries
that required manually generated lookup tables or spatial buffers, Munizaga et al (2011) automate the process by integrating a database with custom software.6 To account for bus-route circuity and for the absence of
trip direction information in Transantiago’s AVL and AFC data, the closeststop rule was modified to minimize a generalized time value, which accounts for both the distance between stops as well as the time savings realized by alighting at an earlier stop and walking. With roughly six million
fare transactions per weekday on the combined bus and metro network,
the process took several hours to complete but inferred approximately 83
percent of bus destinations.
3.2
Origin Inference
The origin-inference process builds upon previous research by incorporating and refining some of the aforementioned assumptions while taking
advantage of additional features in the iBus data set. Performance is ad6 Barry et al (2009) automate an OD-inference process which does not use AVL data.
34
dressed by implementing the process entirely in an object-oriented programming language and tuning the code for speed and memory efficiency.
To enhance compatibility with databases and analysis tools, data are input
and output in an easily parsable comma-separated text format.
Although the three inference processes are conceptually performed
in series, they are combined in a single application which performs some
parts of each in parallel for efficiency. For clarity, however, the origininference algorithm is treated in this section as a discrete process.
3.2.1
Exploratory Analysis of Input Data
Following Zhao et al (2007), Cui (2006), Wang et al (2011), and Munizaga
et al (2011), this methodology infers bus origins by matching AFC and AVL
data. While Oyster and iBus are the only data sets used in the origininference process, additional data from TfL’s Bus Contract Management
System (BCMS) are used in this section to empirically demonstrate the relationship between the AFC and AVL data.
Wang (2010) notes that Oyster timestamps are truncated to the minute,
and that their seconds component can be treated as a random variable
uniformly distributed between 0 and 59, inclusive. By adding 30 seconds
to each Oyster timestamp, an approximation of the expected time value
can be obtained. The BCMS system records fare-transaction data in tandem
with the Oyster system, but contains only a subset of Oyster’s fields. The
BCMS data do, however, retain the seconds component of the transaction
time, and can be used to illustrate the error introduced by the truncation
of Oyster timestamps.7 Figure 3-1 shows the effect of this truncation, as
all fare transactions are effectively shifted to the midpoint of the minute
in which they occur.8
The figure also illustrates the times recorded by the iBus system,
which usually include both arrival and departure timestamps (Wang et
al had access only to departure times). In most cases BCMS shows fares being paid between an iBus event’s arrival and departure times, and these
7 Although the matching of BCMS and Oyster data would improve the resolution of Oyster timestamps, the inefficiencies of obtaining and processing complete daily sets of BCMS
data (in addition to the large Oyster and iBus data sets) do not justify this relatively minor
improvement.
8 Data displayed for a single bus serving a portion of route 38 eastbound, on Saturday, 26
February 2011
35
ure:
nsaction
Grosvenor
Gardens
Victoria Bus Station
Grosvenor
Gardens
Victoria Bus Station
Bus arrival
Hyde
Wilton Park
Street Corner
Old Park Lane/
Hard Rock Café
Green Park
Station
Bus departure
BCMS Transaction
Bus arrival
Hyde
Wilton Park
Street Corner
Old Park Lane/
Hard Rock Café
Green Park
Station
Bus departure
BCMS Transaction
Oyster transaction
+  seconds Old Bon
Royal A
Oyster transaction
+  seconds
:
:
:
:
:
Old Bond Street/
: Royal
:
:
Academy
:
:
:
:
:
Piccadilly
:
:
:
:
Circus :Trocadero/Haymarket
:
:
:
Gerrand Place/
:Chinatown
:
:
:
:
:
Tottenham Court
Road Station
: Street
Denmark
saction
s
:
:
:
:
:
:
:
:
:
:
:
:
:
:
Figure 3-1. Oyster, BCMS, and iBus data for a portion of a single bus (route 38 eastbound).
transactions are often concentrated near the arrival. This coincides with
the expected behavior of customers waiting at a bus stop and then paying their fares in succession after the door opens. Some of these clusters
of transactions begin a second or two before the arrival event (e.g., Trocadero, Gerard Place), possibly due to a difference between the Oyster
reader’s internal clock and that of the iBus system, which is synchronized
to GPS satellites. These small differences are unlikely to have any effect
on the origin-inference process, but two of the stops shown have a more
significant offset.
Two BCMS transactions occur approximately 40 seconds before the iBus
record at the Piccadilly Circus stop, which contains the same time for the
bus’s arrival and departure. As noted in section 2.2.2, iBus will use the
same time as a stop’s arrival and departure if one or the other are missing
or if the bus passed the stop without opening its doors (Robinson 2007). It
is therefore unclear whether this iBus record marks the time at which the
bus drew near to the stop, opened or closed its doors, or departed. Since
separate arrival and departure times were recorded for the previous stop,
it is perhaps most likely that the Piccadilly event represents the door clos-
36
:
:

ing or departure time, and that the customers in question boarded at that
stop after an unrecorded arrival or door-opening event.
The other anomaly in the example is that two customers appear to tap
their cards between the Grosvenor Gardens and Wilton Street stops, approximately 20 seconds before the latter. Since distinct arrival and departure times are recorded for both stops, iBus is assumed to have recorded—
at a minimum—the times during which the doors were open, or an even
earlier arrival (or later departure) time if the system used GPS or odometer
data in the absence of door events. In either case, the AVL system indicates
that the customers tapped their cards between the two stops. It then follows that either the customers boarded at Grosvenor Gardens (or earlier)
and did not tap their cards until after the bus was in transit, or that there
was an unexplained error in the recording of one or more iBus events or
both Oyster events.
The first step in Wang’s (2010) origin-inference method, in which Oyster and iBus records are matched if they occur during the same minute, is
accurate only if no more than one iBus event occurs during that minute.
This criterion is satisfied in the cases shown in Figure 3-1, but the Wilton
Street and Hyde Park Corner stop events are very close to sharing the
same minute, and it should be expected that multiple stops would occur during the same minute when viewing a large sample of iBus data.
That condition is therefore not applied in this methodology: instead, all
available data are matched using Wang’s second criterion, in which Oyster
transactions are assigned to the iBus event closest in time.
But an additional test can be applied before testing for temporal proximity. The AVL data used in Cui’s (2006) study contained door opening
times while the data used by Wang contained only departure times, although iBus departure times can in some cases be ambiguous. Since the
iBus data used in this study contain both arrival and departure times
(notwithstanding the occasional ambiguities), Oyster transactions can in
many cases be observed to fall between an iBus record’s arrival and departure times, obviating the need for further temporal tests.
Almost all BCMS records in Figure 3-1 fall between iBus arrival and
departure times, but a degree of error is introduced by the use of Oyster
data. The fare transactions shown by BCMS to have occurred while the bus
was stopped at Victoria, Old Park Lane, Gerrard Place, and Tottenham
Court Road still occur during these stops when viewed through their
corresponding Oyster records. But other transactions recorded within
37
stops by BCMS are shifted between stops, such as the taps at Grosvenor
Gardens or Hyde Park Corner.
For the Oyster transactions that do not occur within an iBus event,
applying Wang’s rule of matching Oyster data to the temporally closest
iBus event would result in almost the same result as if the Oyster data
contained the BCMS timestamps. The boardings at Green Park, for example, would be shifted from within their stop event to a time before it,
but would still be matched to the same event due to their temporal proximity. In the example, the sole difference between the Oyster and BCMS
approaches occurs between Grosvenor Gardens and Wilton Street. The
two fare transactions, shown by BCMS to occur only a few seconds apart,
occur during two different minutes. The earlier transaction is shifted back
roughly 30 seconds, assigning it to Grosvenor as the closest stop rather
than Wilton, while the later transaction is shifted past Wilton, which continues to be its closest match.
Based on the example in Figure 3-1, it appears reasonable to assume
that the correct stop can often be inferred using Oyster and iBus data, but
that in some cases the previous or next stop will be chosen instead.
3.2.2
Origin-Inference Methodology
Central to the origin-inference process is the matching of AFC and AVL
data. Since there is a many-to-one relationship between fare transactions
and stop events (each boarding is associated with a single stop event but
each stop event can be associated with multiple passenger boardings), the
relationship can be specified by the fare transaction. The following methodology is therefore performed once for each Oyster record.
The origin-inference process is described in Figure 3-2. If an Oyster
record represents a bus transaction, and if iBus data exist for the specified route and trip number, 30 seconds are added to the Oyster start time,
which is compared to the list of stops for the specified route and vehicle
trip. If the offset Oyster time falls between a stop event’s arrival and departure time (as observed at Gerrard Place in Figure 3-1), that stop event
is selected. If the Oyster transaction occurred between two stops, it is
assigned to the closer of the previous stop’s departure time and the next
stop’s arrival time. If the Oyster transaction occurs before the first stop,
the first stop is chosen. If the Oyster transaction occurs after the final stop,
or if the final stop was chosen because the Oyster transaction preceded it
38
Determine mode
[rail or tram]
[bus]
Is start time recorded?
[no]
Do iBus data exist for the route and trip
specified by the Oyster record?
[yes]
[no]
Is the Oyster time greater than
the final stop's arrival time?
[yes]
[yes]
[no]
Is the Oyster time less than
the first stop's arrival time?
[yes]
[no]
Does Oyster time fall between one
stop's arrival and departure times?
Select first stop
[yes]
[no]
Select
current stop
Oyster tap occurred between two stops.
Which one was it closest to in time?
Select
previous stop
[prev]
[next]
Select next stop
Dis
Is the time between the Oyster
end time and the selected iBus
stop event time greater than the
boarding error tolerance?
[yes]
[no]
Origin already
known
Origin unknown
Origin inferred
Figure 3-2. Activity diagram of the origin-inference process.
39
and was closest to it, no boarding location is inferred because it is assumed
that riders do not board at the final stop of a vehicle trip.
If a stop event was chosen during the previous step, an origin-inference
error is calculated. This value is defined as the difference between the offset
Oyster time and the closest iBus time: the arrival time if the next stop is
chosen, the departure time if the previous stop is chosen, or zero if the
Oyster transaction falls within a stop event. The origin-inference error
is appended to the Oyster record, and if the magnitude of the error is
greater than the user-defined maximum, the boarding location is considered to be unknown. Otherwise, the bus stop ID of the chosen stop event
is appended to the Oyster record as the inferred boarding location.
3.2.3
Origin-Inference Results and Sensitivity Analysis
The origin-inference processes was implemented as a Java application (discussed in Chapter 6) and was tested using complete daily sets of Oyster
and iBus data for all of Greater London over ten consecutive weekdays
(the 6th through 10th and 13th through 17th of June, 2011). Each day’s
data consisted of 6.1 to 6.5 million bus transactions, with a daily average
of 6,295,634. The software performs the origin-, destination-, and interchange-inference processes on one day’s worth of data in less than 30 minutes, although the origin-inference portion is typically finished within
the first five minutes.9 The program keeps a running total of origin-inference statistics, which are written to a report after all Oyster records have
been processed. The contents of this report are shown in Figure 3-3 and
in tables Table 3-1 and Table 3-2
Table 3-1 shows the rates at which the various origin-inference rules
were used to match or discard Oyster data.10 Less than two percent of
transactions were discarded because of incomplete or missing data, while
nearly 28 percent of all Oyster bus transactions, after timestamp truncation and the 30-second offset, fell between a bus’s arrival and departure
time.
9 The software was run on a 2.8 gigahertz Intel Core i7 machine with 8 gigabytes of RAM running the Windows 7 operating system.
10 The statistics in Table 3-1 were averaged over the ten-weekday period, and were measured
before the application of the maximum origin-inference error parameter.
40
Table 3-1. Detailed origin-inference results, ten-day average.
Result
Count
Percentage
Cannot infer because bus route data not included in
Oyster record
36,210
0.57%
Cannot find route in iBus data
24,490
0.39%
Cannot find trip in iBus data
61,123
0.96%
Within stop event
1,764,673
27.82%
Between stop events; closer to previous
1,632,375
25.73%
Between stop events; closer to next
2,238,488
35.29%
487,865
7.69%
98,561
1.55%
Before first stop event
After last stop event
Total
6,343,784
Figure 3-3 shows the distribution of origin-inference error for the
ten-weekday period (the daily average distribution can be observed by
simply dividing the frequencies by ten).11 The distribution is discontinuous at zero because it is piecewise defined: as discussed in section 3.2.2, the
negative portion of the distribution is calculated using iBus arrival times,
the positive portion is calculated using departure times, and the frequency
of 18.4 million at zero accounts for the Oyster transactions that occurred
between iBus arrival and departure times.
Beyond its discontinuity, the origin-inference error histogram exhibits two other notable traits. First, the distribution is visibly skewed to the
left. Since negative error values indicate Oyster transactions that occurred
before iBus arrival times, this rise in the left tail fits the expected result
of more Oyster transactions occurring near a bus’s arrival time than its
departure time, as illustrated with the Victoria stop in Figure 3-1. Second,
the distribution rises sharply near -30 seconds and falls sharply again at 30.
This resembles the expected result of adding the aforementioned skewed,
discontinuous distribution to a uniformly distributed function on the interval [-30, 30]. The uniformly distributed component of the curve in
11 Negative errors in the histogram denote Oyster transactions that occurred before the closest bus arrival, while positive errors indicate transactions that occurred after the closest bus
departure. Oyster transactions that occurred between a bus’s arrival and departure times are
assigned an error of zero.
41
1,400,000
18,400,000
1,200,000
18,200,000
1,000,000
1,000,000
800,000
800,000
cause I can. So, please, let me do it this way. Like most urban public bus
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600,000
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Figure 2.4. Distribution of error between Oyster and iBus timestamps: The error is the
minute of the Oyster transaction, plus 30 seconds, minus the iBus time (which contains
200,000
200,000
seconds).
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30-second offset of the minute-resolution Oyster
timestamp will occur up to 30 seconds before the arrival time or up to
30 seconds after the departure time. For example, Figure 3-5 shows that
a rider tapped after the bus’s arrival at Grosvenor Gardens but before its
departure. Since the stop event did not span the midpoint of the minute
(18:54:30), the offset Oyster record appears a few seconds before the arrival time, introducing a small error.
In rare cases, bus route or trip data are recorded incorrectly, causing
some Oyster records to be erroneously matched to iBus records several
hours away. This noise adds long tails to the origin-error distribution,
resulting in 96.0 percent of origin-inference error values falling within a
tolerance of ±2 minutes. Increasing this3 threshold to ±5 minutes raises the
proportion by only 1.4 percent, illustrating that this choice of thresholds
sits well beyond the sensitive portion of the parameter’s range. While the
choice of this five-minute margin can be seen as arbitrary, it seems reasonable to expect an occasional five minute difference between AFC and AVL
times, especially since buses sometimes stop for several minutes at busy
42
stations or signals during peak hours, and because customers might in
some cases tap their cards after the bus has left the stop. The five-minute
boarding-error tolerance (also used by Cui [2006] and Wang et al [2011]) is
therefore applied in this study. This value, however, can be easily changed
by the user at runtime.
The results of the origin-inference process on the ten-day sample, after the application of the five-minute error threshold, are summarized
in Table 3-2. The process was unable to infer boardings for 1.36 percent
of bus transactions, while an additional 2.61 percent were matched but
excluded because their origin-inference errors exceeded the five-minute
limit. The origins of the remaining 96 percent of Oyster bus trips are
inferred, for an average of approximately six million bus stages per day.
3.3
Result
Count
Percentage
Not inferring bus boarding because Oyster
record was not matched to an iBus record
85,441
1.36%
Not inferring bus boarding because the time
between its boarding tap and the nearest
iBus event is beyond ±5 minutes
164,566
2.61%
Bus stages with origin inferred within ±5
minutes
6,045,627
96.03%
Total
6,295,634
Destination Inference
Like the origin-inference process, the process for inferring bus alighting
times and locations builds upon previous research to create a more robust
and efficient algorithm. As stated in the previous section, the three sequential processes are performed partly in parallel for efficiency, but the
destination portion is presented here as a logically discrete process for the
sake of clarity.
43
3.3.1
Input Data
The primary input to the destination-inference process is the origin-inferred Oyster data, to which the rider’s inferred destination will be appended. The process is founded on the two key assumptions introduced
by Barry et al (2002): that the best estimate of a rider’s alighting location
is the stop closest to that rider’s next origin, and that the best estimate of
a rider’s final daily destination is that rider’s first daily origin. The destination-inference process therefore requires spatial information about the
rider’s possible alighting locations and about her subsequent origin. In the
case of London, this information is contained in two data sets: one of bus
stops and another of rail stations, including Underground, Overground,
National Rail, DLR, and Tramlink modes. The fourth input to the process
is iBus data, which is used to infer alighting times as demonstrated by
Wang et al (2011).12
The only information required of stations are the unique identifiers
by which they are referenced in the Oyster data—which in this case is the
British National Location Code (NLC)—and the station’s Cartesian coordinates. Station coordinates were copied from GIS data obtained from TfL
but were then converted from geographic coordinates (measured in angular degrees) to linear coordinates (measured in meters) using the British
National Grid projected coordinate system. The benefits of this conversion are, first, that it avoids the spatial distortion that results from the convergence of meridians at non-equatorial latitudes (such as London’s) and,
second, that meters provide a satisfactory resolution for pedestrian-scale
transportation analysis (which is required for the inference of alightings
and interchanges). By contrast, the use of degrees of latitude and longitude would require the processing of real numbers rather than integers,
which would double or quadruple the amount of hardware resources required to process spatial data13.
Bus stop data similarly require only a unique identifier and spatial coordinates. The “stop code” field from the BusNet system is used, since it
is guaranteed to be unique and to persist indefinitely. iBus data, which
12 See section 3.1.2.
13 The software stores metric coordinates and distances using Java’s short and int data types,
which comprise 16 and 32 bits, respectively. Real numbers would be processed using the
64-bit double data type.
44
use their own internal, temporary bus stop identifiers, are assigned the appropriate BusNet stop codes when exported from TfL’s servers, enabling
Oyster and iBus data to be matched in the software. Each stop’s spatial
coordinates are stored by BusNet in the British National Grid coordinate
system, which is why that system in particular (among many projected
metric coordinate systems) was chosen for the station data.
Data were obtained for 21,554 bus stops, which describe the locations
of each bus stop’s pole marker. In many cases, multiple stops are clustered
near a single logical node on the transport network, such as an intersection or rail station (Figure 3-4 and Figure 3-5). Data were also obtained for
Figure 3-4. Letter-designated bus stops near Elephant and Castle Tube station (source: TfL).
Figure 3-5. Elephant and Castle stops S and T, each having its own pole marker and shelter.
45
1,435 stations of various rail modes, 1,361 of which were spatially unique
(in some cases multiple codes relate to different modes or gate clusters at
the same physical station). These stops and stations are mapped in Figure
3-6, which encompasses all of Greater London. Most of the stations beyond the extent of the bus network belong to the National Rail system,
which extends through all of Great Britain.
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N
0
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Figure 3-6. Stop and station locations in Greater London.
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Bus stop
Rail station
!
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3.3.2
Destination-Inference Methodology
The destination-inference process draws from several of the methodologies described in section 3.1.2, using a refined processing algorithm to distill the destinations of over six million passenger bus trips from roughly
16 million Oyster transactions and five million iBus records in less than
ten minutes.
The logic of the destination-inference process is built upon Barry et
al’s (2002) two assumptions: that riders alight at the stop closest to their
subsequent transaction location or, in the case of the last trip of the day,
that they alight at the stop closest to their first daily origin. These assumptions will be referred to herein as the closest-stop rule and the daily-symmetry
rule, respectively.
It should be apparent to anyone who has ridden an urban bus, however, that there are many cases in which these two assumptions are not
valid. The closest-stop rule, for example, is regularly violated by any rider
who alights a bus, walks alongside the bus route to visit two or more
businesses, then makes his next fare transaction at a stop or station that
is closer to some other stop on the previous route than it is to the stop at
which he actually alighted. The daily-symmetry rule is similarly violated
by any rider whose final Oyster trip of the day ends somewhere other
than his first Oyster origin, whether because he started and ended his day
in different places, or because his first trip was preceded (or his last trip
was followed) by a non-Oyster trip, such as a bicycle or taxi ride. Despite
these shortcomings, the closest-stop and daily-symmetry rules have been
shown to provide a reasonable approximation of riders’ alighting locations, as discussed in section 3.1.2. In the absence of observed alighting
information, Barry et al’s assumptions reflect our best estimates of where
riders might have ended their bus trips.
The closest-stop and daily-symmetry rules necessitate the calculation
of several distances. The determinant location—the origin of the subsequent trip or the first origin of the day—shall be referred to as the target
location. The distance between the rider’s target location and each stop
served by the current vehicle trip’s pattern (a distinct sequence of bus stops
served by one or more vehicle trips on a bus route) must therefore be
calculated in order to determine which stop is closest. To accommodate
these calculations, the set of all stops within each pattern, and the set of
47
all patterns within each route, are distilled from the daily set of observed
iBus data before any destinations are inferred.
Once spatial information is known for all bus patterns, the algorithm
illustrated in Figure 3-7 is applied to each Oyster record. First, duplicate
Oyster records are discarded, since riders might sometimes tap a card in
error before making a successful transaction. Valid non-duplicate transactions are then processed according to their mode: rail destinations should
already be known, tram destinations are not inferred because AVL data are
unavailable, and bus transaction are passed to the next step in the process
so that their destinations might be inferred.
If a bus transaction is the only record associated with the card that day,
it is assumed that any other travel that day was made without the Oyster
card—possibly on a non-Oyster or private transport mode. The destination of the journey stage is therefore unknown. If there was more than
one transaction made with the card that day, however, the current stage’s
origin-inference error is tested. If the magnitude of the error is greater
than the maximum origin-inference error (specified by the user), the origin is considered to have been unreliably inferred, and it is assumed that
the destination cannot be reliably inferred either.
If the bus stage’s origin has been satisfactorily inferred, the target location for the destination-inference process is defined. If the journey stage
is the cardholder’s last that day, the origin location of his first trip is selected as the target location. Otherwise, the origin of the cardholder’s
subsequent trip is selected.
If the target location is unknown, or is a bus boarding that was not inferred within the maximum origin-inference error, the destination of the
current trip is considered unknown. Otherwise, the algorithm attempts
to find spatial coordinates for the current bus pattern and for the target
location. If the coordinates of either the target node or the pattern are
missing, an alighting location is not inferred.
48
Is the record a duplicate?
[yes]
[no]
Determine mode
[bus]
[rail]
Are there any other records in
the card's daily travel history?
[tram]
[no]
[yes]
Has the record's boarding location been inferred
within the user-specified boarding-error tolerance?
[yes]
[no]
[yes]
Define "target location" as origin
of first trip of daily travel history
Is the current stage the
final stage of the card’s
daily travel history?
[no]
Define "target location" as origin
of next trip in daily travel history
Is the target location known, or has it
been inferred within the user-specified
boarding-error tolerance?
[no]
[yes]
Are spatial coordinates known for the current stage's
bus route/pattern and for the target location?
[yes]
[no]
Select the next closest
bus stop on the current
route/pattern
Select the bus stop on the current
record's route and pattern that is
closest to the target location.
Is the bus traveling away from the rider's next tap location?
[yes]
Alighting time and
location unknown
(not inferred)
Destination and
end time
already known
Skip record
Alighting time and
location inferred
[no]
Is the selected stop within the maximum
interchange-inference distance of the next
tap location?
[no]
[yes]
Did iBus record a stop event
for the associated route and
trip at the selected bus stop?
If the current stage is not the last stage
of the day, was there enough time for
the passenger to have alighted at the
chosen stop and to have transferred
to the target location?
Assume alighting time to
be iBus stop event time
[yes]
[no]
[yes]
[no]
Figure 3-7. Activity diagram of the destination-inference process.
49
Next, the closest-stop and daily-symmetry rules are applied as illustrated in Figure 3-8. If a rider was inferred to have boarded a bus at stop 1,
and if her next tap occurred at station Y, stop 5 would be selected as her
tentative alighting location because it is the closest to Y. If her second tap
occurred on another bus route rather than at a rail station, the same logic
would apply: if her next journey stage was inferred to have originated at
stop X, stop 4 would be selected as her tentative alighting location. If the
current trip was the cardholder’s last of the day, and if her target location
(i.e., her first origin of the day) was stop 6, she would also be inferred to
have alighted there for her final trip.
At this point, it is possible that the candidate alighting location is not
a feasible destination for the cardholder. For example, if the cardholder
was inferred to have started her current bus trip at stop 5, but if her subsequent transaction was a bus boarding at stop X, the bus was moving
away from her subsequent boarding location: the closest stop (stop 4) was
already served by the bus before she boarded. It is possible in this case that
one or both of the origins were inferred erroneously, or that both origins
are correct but that the rider did not alight at the closest stop to her next
Oyster transaction, making the closest-stop rule inapplicable. To handle
2
bu
sr
ou
te
next station entry
Y
next bus boarding X
or
6
5
4
3
2
1
bu s r
oute
o
rre
f cu
nt
n
jour
age
ey st
tu
be
lin
e
Figure 3-8. Example of destination inference for a passenger trip on a bus route (green) followed by another bus trip (red) or by a rail trip (blue).
50
this scenario, destinations are not inferred if the candidate alighting location is the same as its journey stage’s boarding location, or if the candidate
alighting location was served by the bus prior to its serving of the rider’s
boarding location.
The closest-stop rule is further checked against the distance between
the candidate alighting node and the target node. If the closest stop on the
current route is far from the cardholder’s subsequent transaction location,
it is assumed that proximity between the two stages was not a priority
for the cardholder, and that the nearest-stop rule is therefore inapplicable.
Thus, if the distance between the candidate alighting location and the target location is greater than the user-specified maximum destination-inference
distance, the destination is not inferred.
If the candidate alighting node has passed all of the previous tests, an
iBus record is sought for the bus route and trip (both of which are specified by the Oyster record) at the candidate alighting location. If found,
the iBus record’s arrival time is used as the candidate alighting time. If an
iBus record cannot be found, the next-closest bus stop to the target node
is chosen as the candidate alighting node, and testing is repeated starting
with the determination of whether the bus was traveling away from the
subsequent transaction.
Once a candidate alighting node passes all of the aforementioned tests,
a final test is applied which checks whether the rider could have walked
from the candidate alighting location to the target location in the available time. For example, if stop 4 in Figure 3-8 was chosen as an alighting
location because it was the closest to stop X, but if the bus arrived at stop
4 several minutes after the rider boarded at stop X, it is likely that the rider
alighted at an earlier stop. Stop 3 is then tested as the possible alighting
location, because it was the next closest and next earliest, and therefore
might have provided enough time for the rider to subsequently board
at stop X. This test is not applicable for the rider’s final trip of the day,
since the duration of her egress is unknown. For any other transaction,
however, the assumed minimum time required to move between the two
locations, t*, is calculated as:
51
d –ε
t* = —
R
where
d is the Euclidean distance between the candidate alighting location
and target location,
R is the user-specified maximum interchange speed,
ε is the maximum boarding-error tolerance.
The maximum interchange speed is the greatest speed at which a person
is assumed to be able to walk (or run) in the urban environment, taking
into account that d is the Euclidean rather than actual distance (i.e., the
path taken is likely not straight), and that the pedestrian might be delayed
by street crossings or other impediments. The maximum boarding error
tolerance is then subtracted to account for the iBus arrival time not necessarily being the time at which the rider alighted. This parameter is used
in this equation because it is assumed that the amount of error between
a cardholder’s alighting and its associated bus arrival event is generally
similar to the amount of error between his boarding (and fare transaction)
and its associated arrival event.
To test whether it was feasible for the rider to have alighted at the
inferred location with enough time to walk to and tap at the subsequent
location, her inferred alighting time, t1, is compared to her subsequent
transaction time, t2, as follows:
t1 + t* < t2
If the expression is true, the passenger is inferred to have alighted at
the specified location and time. But if the candidate time and location
fail the test (i.e., if the expression is false), the next-closest stop becomes
the candidate alighting location, and previous tests are repeated using the
new candidate, starting with the determination of whether the bus was
traveling away from the subsequent passenger origin.
3.3.3
Destination-Inference Results and Sensitivity Analysis
As discussed in section 3.2.3, the origin-, destination-, and interchangeinference processes were tested together using complete daily sets of Oys-
52
ter data for all of Greater London over ten consecutive weekdays.14 Destination inference was completed within the first fifteen minutes of the
program’s execution (along with origin inference and part of the interchange-inference process). The number of destinations inferred, however,
depends upon the setting of three user-defined parameters.
The first parameter, maximum walk speed, addresses to the speeds at
which passengers travel when they are not in the public transport system
(calculated as a function of time and the Euclidean distance traveled). Figure 3-9 illustrates the distribution of average speeds between bus or rail
destinations and their subsequent rail origins.15 The prevalence of average
speeds below one kilometer per hour reflects passengers who performed
activities between journey stages. Someone who was at his workplace for
nine hours, for example, and who arrived and departed on the same route
(having inbound and outbound stops on opposite sides of the street, 20
meters apart), would have an observed speed of 0.002 kilometers per hour.
Frequency










,
,
,
,
,
,
,
8,000
Average Speed between station exit or inferred bus alighting and subsequent station entry (meters per hour)
Figure 3-9. Distribution of average out-of-system speeds.
14 The data were obtained for the 6th through 10th and 13th through 17th of June 2011.
15Speeds are calculated as Euclidean distances between station exits or inferred bus alightings
and subsequent station entries. Subsequent bus boardings are excluded to eliminate wait
time from the calculation.
53
,
The second most prevalent cluster of speeds is observed between 3 and
7 kilometers per hour, and likely reflects the speed at which passengers
made interchanges.
Ninety percent of passengers exhibit out-of-system speeds of less than
two kilometers per hour, which likely reflects activities such as work or
school, or return trips that start where the previous trip ended. The 90th
percentile speed is approximately 5 kilometers per hour, while 8 kilometers per hour reflects the 99.8th percentile. Since the maximum walkspeed parameter is used to test whether passengers had enough time to
alight a bus before walking to their next transaction locations, the value
of 8 kilometers per hours was chosen because it sets a conservatively high
threshold for eliminating passengers from the destination-inference process. This speed is significantly higher than most of the speeds typically
observed between Oyster trips, yet lower than any unreasonably high
speeds that might indicate vehicle travel (perhaps by taxi, private automobile, or non-Oyster public transport).
The second parameter, boarding-error tolerance, is used by both the
origin- and destination-inference processes. As described in the previous
section, this amount of time is added to the maximum walk speed value
to account for the errors typically observed between Oyster and iBus
times. The sensitivity of this parameter and its setting of five minutes
were described in section 3.2.3.
The final parameter used to estimate alighting locations is the maximum destination-inference distance. When the distance between a bus
alighting location and the rider’s subsequent fare transaction is great
enough, it is reasonable to expect that the closest-stop rule does not apply.
A distance of several kilometers, for example, could indicate that the rider
took a non-Oyster transport mode between the two journey stages, and
therefore did not necessarily alight at the stop closest to the subsequent
fare transaction location. A more moderate distance such as 1.5 kilometers
might have been traversed by foot, but the difference between any two
consecutive potential alighting stops would be relatively small in comparison to the total distance walked by the cardholder during that segment of
her journey, making its proximity to the next tap less important.
A maximum destination-inference distance should therefore be
chosen that excludes longer values which might be less relevant to the
closest-stop rule while retaining the shorter inter-stage distances which
constitute the majority of observed Oyster activity. The distribution of
54
out-of-system distances is illustrated in Figure 3-10,16 and a list of their
cumulative frequencies is shown in Table 3-3. A value of 750 meters was
chosen as the maximum destination-inference distance, which enables
over 94 percent of observed bus stages to potentially be included in the
results while avoiding the small marginal gains that would be realized by
using a greater distance.
,
Frequency
,
,
,
,
,
,








 750 
Distance between subsequent tap and closest stop on current route (meters)

,
Figure 3-10. Distribution of Euclidean distances between candidate alighting stops and next
fare transaction.
Table 3-3. Cumulative frequencies of observed inter-stage distances.
Euclidean distance
(meters)
Percentile
Marginal
change
500
90.5%
750
93.5%
3.5%
1,000
95.2%
1.7%
1,500
96.9%
1.6%
2,000
97.6%
0.7%
2,500
98.0%
0.4%
16 The candidate alighting stop is the closest stop on a cardholder’s bus stage to his next Oyster transaction, regardless of whether that stop was eventually discarded by the destinationinference process in favor of another stop.
55
The rates at which bus alighting times and locations were inferred over
the ten-day sample period are shown in Table 3-4.17 The table also indicates the frequency with which passenger bus stages were excluded from
the process by each of the algorithm’s tests. Since failure of any one test
can deem a record’s destination “not inferable,” tests are presented in the
order in which they are applied: later tests can only exclude stages that
have already satisfied the conditions of all earlier tests.
While most tests immediately eliminate transactions that do not satisfy their conditions, the two tests that are applied recursively are treated
separately at the bottom of the table. If iBus data exist for the cardholder’s route and vehicle but not for the candidate alighting node, or if there
was not enough time for the rider to have alighted at the candidate location before her next transaction, the program resumes the destination-inference process using the next-closest stop. This process is repeated until
a destination is successfully inferred, until one of the non-recursive tests
is failed, or until all stops on the vehicle trip have been tested. The table
indicates the frequencies at which the recursive tests were applied (counting each bus transaction only once, regardless of how many of its stops
were tested). Most recursively tested bus stages were either successfully
inferred or were eliminated by another test: only thirteen transactions
per day typically exhaust all stops on the observed bus pattern during
recursive testing.18
On average, destinations were inferred for 75.62 percent of Oyster
bus stages, or roughly 4.7 million bus stages per day. Approximately 28
percent of the excluded bus stages (seven percent of total bus stages) could
be retained by improving the origin-inference process or by widening its
tolerance, while additional stages could be retained (at the expense of accuracy) by adjusting the maximum walk-speed and distance parameters.
17The process was executed using a maximum boarding error of ±5 minutes, a maximum
walk speed of 8 km/h, and a maximum destination-inference distance of 750 meters.
18 For example, if a rider boarded at the third to last stop, and if iBus events were not recorded
for the last two stops, the transaction would exhaust all possible alighting locations and
thereby fail all recursive tests.
56
Table 3-4. Destination-inference results:
Result
Count
Destination not inferred because:
Only one stage on card that day
305,351
4.85%
Current stage's boarding location not matched to
an iBus record
80,707
1.28%
Current stage's boarding location inferred beyond
allowable error (±300 sec.)
156,580
2.49%
Next stage's boarding location not matched to an
iBus record
55,620
0.88%
Next stage's boarding location inferred beyond
allowable error (±300 sec.)
151,184
2.40%
Last stage of day; rider traveling away from first
origin of day
203,238
3.23%
Rider traveling away from next origin
230,657
3.66%
Last stage of day; distance between candidate
alighting location and first origin of day greater
than 750 meters
226,571
3.60%
Distance between candidate alighting location and
next origin greater than 750 meters
197,873
3.14%
12
0.00%
Subtotal, destination not inferred
1,607,793
25.54%
Destination inferred
4,687,842
74.46%
Total bus stages
6,295,634
All stops in pattern failed recursive tests (see below)
Recursive tests:
iBus did not record an event for the nearest stop;
tested next-closest stop
54,593
0.87%
Not enough time for rider to have alighted at the
nearest stop; tested next-closest stop
20,424
0.32%
57
3.4
Summary
The methods presented in this chapter are shown to infer bus boarding
locations in 96 percent of cases and of bus alighting times and locations
in 76 percent. Most uninferred boardings are due to the Oyster and iBus
data not matching within the five-minute threshold, although this parameter could be changed after additional validation. Further analysis should
be conducted to test whether a more relaxed maximum boarding-error
tolerance would be reasonable. The origin-inference tolerance doubly affects the destination-inference process, since origin locations are required
both for the journey stage being processed and for the subsequent stage.
The software developed to execute this algorithm is discussed in detail
in Chapter 6 and its outputs are used in the interchange-inference and
full-journey matrix-expansion processes in Chapters 4 and 5. Applications of this algorithm, and of the algorithms that utilize its outputs, are
presented in Chapter 7.
58
4
Interchange Inference
The origin- and destination-inference processes discussed in the previous
chapter enrich the Oyster data set by adding time and location information to most bus boardings and alightings. These enhanced bus records
can then be analyzed along with Oyster rail transactions from the same
card, enabling the observation of passengers’ full journeys or daily travel
histories.
This chapter presents a method for inferring whether each of an Oyster card’s journey stages was linked to the next through an interchange (or
transfer1). Doing so enables the analysis of full passenger journeys—including those that span multiple public transport modes—which are important descriptors of travel demand, and are critical for transport planning at the network level.
In addition to its value for providing full-journey information, this
work is also useful for studying interchanges themselves. By yielding
quantitative information about the distances and durations of interchanges—two key determinants of interchange disutility (Hine et al
2003)—this work can complement qualitative studies of interchange facilities to help planners improve the transfer experience, thereby reducing
barriers to cross-modal travel and enabling cities and their residents to
enjoy the social and economic benefits of increased mobility and accessibility (Guo and Wilson 2007, TfL 2001, 2002, 2009).
1 The terms transfer and interchange will be used interchangeably.
59
4.1
Concepts and Terminology
Before discussing the interchange inference process, it is necessary to define a few terms and concepts used in this research. First, since this chapter addresses the problem of inferring interchanges between AFC journey
stages, it is necessary to clarify the concept of a journey stage. At many
London Underground stations, for example, an Oyster cardholder can
alight a train, walk to another platform (serving a different Underground
line), and board another train without tapping out or leaving the station.
By convention, this is informally referred to as an interchange, but the
change is not recorded by the Oyster system because both of the customer’s train rides occur between his entry and exit taps, thereby associating
both trips with a single Oyster record.2 In the context of this research, an
Oyster journey stage (or simply journey stage) is therefore defined as any
portion of a rider’s travel activity that is represented by a single Oyster
record. Each rail journey stage will therefore include one or more passenger trips, while the requirement that Oyster bus fares or passes be paid
or validated onboard guarantees that each bus journey stage relates to a
single passenger trip.
Second, it is necessary to define the concept of an interchange as it
relates to this work. It should be generally agreed that the passenger in
the previous example, or another passenger who alights a bus and immediately walks to, waits for, and then boards another bus (serving a route
perpendicular to the first for example) both performed interchanges. Alternatively, it should be agreed that if the Tube passenger left the station
and picked up his child from day care before returning to the station and
boarding the second train, or that if the bus passenger collected a package
at the post office before boarding her second bus, neither case would be
considered an interchange. The distinguishing difference in both cases is
the purpose for the transitions between the riders’ trips. In the latter case,
the passengers traveled to the locations mentioned in order to perform
activities from which they derived utility (picking up a child and collect2 The Oyster data set actually contains two records for each rail transaction: one for the entry
tap and another for the exit. These transactions are combined by the software prior to the
interchange-inference process (as described in Chapter 6) and are thus considered a single
record.
60
ing a parcel). In the interchange case, their purpose was simply to board
another transit vehicle in order to derive utility from activities performed
at subsequent destinations.
There are many cases, however, in which the discerning of interchanges from activities may be more subjective. If, for example, a passenger purchases a cup of coffee while walking from her alighting bus stop
to her subsequent bus stop, she is deriving utility from her purchase but
is unlikely to have traveled to that location solely for the coffee—she presumably could have made such a purchase at another interchange location
or at her final destination. While this example might be considered both
an activity and an interchange, the important distinction from a networkplanning perspective is whether the customer chose to travel to that location to make her purchase, or made her purchase at that location because
she happened to be transferring there. If the latter case were true it would
mean that the customer’s demand for travel lies with her ultimate destination rather than the bus stop near the coffee house.
For this research, an interchange is therefore defined as a transition
between two consecutive journey stages that does not contain a trip-generating activity. The passenger may have derived utility from an activity
performed during the transition, but the activity was not reason enough
to make the trip: the primary purpose of the transition was to connect a
previous stage’s origin to a subsequent stage’s destination.
Lastly, the inference of interchanges allows the linking of journey
stages into full journeys. Taking into account the concept of journey
stages and interchanges, it follows that a full passenger journey can be
defined as a sequence of journey stages connected exclusively through
interchanges. Passengers may still transfer between rail lines using a single Oyster transaction, but these “behind-the-gate” interchanges are included within rail journey stages and are therefore not considered in this
algorithm.3
3 Behind-the-gate interchanges could be inferred by integrating a path choice model, such
as those developed specifically for the TfL network (Guo 2008, Paul 2010) or elsewhere
(Raveau et al 2010).
61
4.2
4.2.1
Previous Research
Discerning Activities Using AFC Data
This research aims to infer interchanges by distinguishing them from tripgenerating activities, but activity information is not recorded by AFC systems. By noting the spatial and temporal characteristics of certain activity
types, however, these patterns can be detected in the AFC data.
Holton (1958) distinguishes “convenience goods,” for which proximity to the customer is a key concern, from “shopping” and “specialty”
goods, for which variations in price, quality, or selection are great enough
to warrant dedicated trips. Holton also notes that convenience purchases
(generalized here to convenience activities) are typically shorter in duration than other types. The implications for this research are that convenience activities—which by definition are not trip-generating activities
and which can therefore take place during interchanges—can be partially
distinguished from trip-generating activities by their duration.
Kuhnimhof and Wassmuth (2002) mine a database of travel survey information to detect correlations between activity types and their spatial
and temporal patterns such as duration, frequency, and time of day in
order to calibrate the activity-generating component of a travel-demand
forecasting model. Chu (2010) searches for similar spatial and temporal
patterns in a set of AFC data from a bus network (for which he previously
inferred origins, interchanges, and destinations) to infer general activity
types such as home, work or school, and recreation.
4.2.2
Interchange Inference Using AFC Data
The tendency of trip-generating activities to be generally longer in duration than convenience activities has led several researchers to infer busto-bus interchanges according to the amount of time elapsed between
consecutive boarding transactions or journey stages.4 Bagchi and White
(2004, 2005) assume that two bus stages are linked if they occur on different routes and if both AFC boarding transactions occur within 30 minutes
of one another. Barry et al (2009) and the San Francisco Bay Area’s re4 Thill and Thomas (1987) provide a literature review of early attempts at trip chaining.
62
gional planning organization (Metropolitan Transportation Commission
2003) use the same assumption, while Okamura et al (2004) apply a threshold of 60 minutes, McCaig and Yip (2010) allow 70 minutes, and Hoffman
and O’Mahoney (2005) set an upper limit of 90 minutes.
Seaborn et al (2009) use Oyster boarding transactions to infer interchanges between London buses, and use Oyster station entry and exit
data to infer transfers between bus and Underground. After exploring the
distributions of time for these various journey types, they recommend
thresholds of 20 minutes for tube-to-bus interchanges, 35 minutes for
bus-to-tube, and 45 minutes for bus-to-bus. Seaborn et al then calculate
the distributions of cardholder journeys per day and stages per journey,
and compare both to the London Travel Demand Survey (LTDS).
Chu and Chapleau (2007, 2008) infer alighting times and locations using the GPS location-stamped boarding transactions of other cardholders,
while Wang et al (2011) and Munizaga et al (2011) infer alighting times
and locations using AVL data. All three studies thereby eliminate in-vehicle
travel time from their observations, providing a more accurate estimate
of actual interchange times. Munizaga et al set a 30-minute interchange
threshold between bus stages, while Chu and Chapleau set time thresholds dynamically. By assuming a maximum walking speed of 4,320 meters per hour, they allocate an interchange time threshold as a function
of the distance between the alighting and subsequent boarding locations,
and add a five-minute buffer to prevent unreasonably short time thresholds when the two stops are extremely close together:
interchange distance
+5
Interchange time threshold = —
4,320
63
In addition to using time thresholds to test for interchanges, McCaig and Yip (2010) also apply a spatial condition. After classifying London’s bus routes into four general travel directions (northeast, southeast, southwest, and northwest), they calculate the
elapsed times between pairs of Oyster cardholders’ bus boardings and group the results into the categories shown in Table 4-1.5
Under the assumption that an interchange cannot take place if both transactions occurred on the same route, and testing the assumption that interchanges are not likely to include travel in the opposite direction, they
show that the time distributions are consistent with these assertions
(building upon the additional assumption that interchanges are likely to
be shorter in duration than trip-generating activities). They then add a
spatial condition to their proposed interchange-inference process by imposing the rule that an interchange cannot consist of two transactions in
opposite directions.
Table 4-1. Median elapsed times between Oyster cardholders’ consecutive bus boarding
4.3
Same Direction
Opposite Direction
Same Route
85 min.
> 120 min.
Different Route
30 min.
55 min.
Methodology
The interchange-inference algorithm developed in this thesis builds upon
the research described in the previous section by applying a combination
of spatial, temporal, and binary criteria to a complete set of AFC data collected from a multimodal transport network. Since these tests use tem5 Since McCaig and Yip measured the time between consecutive bus boarding transactions,
the durations shown in the table include both the in-vehicle travel time of the earlier stage
and the true inter-stage time (which includes any interchange, activity, or waiting time
between the two stages).
64
poral and spatial data as proxies for passenger activity information, it is
important to note that there are still certain trip-generating activities that
could be mistaken for interchanges.
Returning to the example in section 4.1, if a rider alighted a bus,
quickly picked up a package at the post office, boarded another bus serving a route perpendicular to the first, and finally alighted at her workplace,
all of the methods discussed in section 4.2.2 would consider her to have
made an interchange. Since the package was at that particular post office,
there was a reason for the rider to visit that bus stop. Even if there were
a direct bus from her origin (her home, for example) to her workplace,
she would still have taken these two journey stages because she intended
to visit that specific post office. In other words, the post office visit was
a trip-generating activity and should not be considered an interchange.6
For this reason, each of the tests applied in this algorithm are used
to determine whether a transition between two journey stages was not
an interchange. Failing any one test will label the former transaction as
not linked to the next, while any transactions that pass all tests can be
considered likely interchanges. Since the previous example illustrates the
possibility of producing false positives, the algorithm errs on the side of
labeling transactions as not linked when interchange indicators are contradictory or ambiguous.
The algorithm requires two inputs: a set of origin- and destinationinferred Oyster data (the output of the OD-inference process), and a matching set of iBus data. The interchange status of each Oyster record is then
inferred by applying the following binary, temporal, and spatial tests, as
listed in Table 4-2 and illustrated in Figure 4-1. If any one test fails, the
transaction is considered “not linked” to the following transaction: all
further tests are skipped for that record and testing resumes with the next
transaction. If a test fails because of missing data, the interchange status
is considered to be “non-inferable.” The record is considered unlinked
6 From an activity-based demand-modeling perspective, these two bus trips were part of the
same tour, since they were not separated by a primary origin (e.g., home) or a primary destination (e.g., work), but were nonetheless two distinct single-stage journeys which included
the post office visit (a trip-generating activity) in the tour. While the journeys inferred in
this work could be grouped into tours, tours are extraneous to the interchange-inference
process itself and are not considered further in this chapter.
65
rather than linked, but the algorithm notes its status as non-inferable for
reporting purposes.
Table 4-2. Tests applied during the interchange-inference process
Binary Conditions
·· Subsequent transaction
·· Successful bus destination inference
·· Complete rail exit information
·· Repeat transport service
·· Subsequent origin inference
Temporal Conditions
·· Interchange time
·· Maximum bus wait time
·· Observed bus headway
Spatial Conditions
·· Maximum interchange distance
·· Circuity
·· Full-journey length
4.3.1
Binary Conditions
Subsequent Transaction. The first condition tested is whether the transaction
was the cardholder’s final stage of the day. If so, it is considered unlinked.
Successful Bus Destination Inference. If the transaction represents a bus stage,
its alighting time and location are required in order to apply the spatial
and temporal tests. If a destination was not inferred for the transaction, its
interchange status cannot be inferred.
Complete Rail Exit Information. Similarly, if the transaction represents a rail
stage, its station exit time and location must be recorded. If not, the transaction cannot be inferred.
66
Is current record the final stage in
its card's daily itinerary?
[no]
Determine mode
[not inferred]
[not recorded]
[tram]
Check inferred alighting
time and location
[yes]
[bus]
Check exit time
and location
[rail]
[recorded]
Is the next record in the
card's itinerary a bus
stage, and is its route the
same as the current
stage's route?
[inferred]
If the next record is a
bus stage, has its origin
been inferred?
[no]
Is the next record in the
card’s itinerary a rail stage, and
is its origin station the same
as the current stage’s
destination?
[no]
[yes]
[yes]
[yes]
[no]
Define "interchange start" as the current stage's destination and “interchange end” as
the next stage's origin. Is the distance between the interchange start and interchange
end greater than the Maximum Interchange Distance?
Interchange
cannot be
inferred
[yes]
[no]
Calculate the maximum interchange time as a function of the Minimum
Interchange Speed, the Minimum Transfer Time Allowance, and this
interchange's distance. Is the time between the interchange start and
interchange end greater than the maximum interchange time?
What is the mode of the next stage?
[rail]
[bus]
[yes]
[no]
Calculate the estimated passenger arrival time as a function of the
interchange start time and the Minimum Walk Speed.
Is the time between the passenger arrival time and the interchange end time greater than the
Maximum Bus Wait Time?
[yes]
[no]
Check iBus data to determine whether the customer boarded the first bus that
arrived after the estimated passenger arrival time.
[boarded subsequent bus]
[boarded first bus]
Calculate the circuity ratio as the total length
of the prospective journey (current stage +
interchange distance + next stage) divided
by the distance between the first segment's
origin and the next segment's destination. Is
this ratio greater than the Maximum Circuity
Ratio?
[Future:] Do iBus-inferred bus-load
data suggest that all buses that
the customer skipped were full?
[no]
[yes]
[yes]
Not linked to
next segment
[no]
Linked to next
segment
Figure 4-1. Activity diagram of the interchange-inference process
67
Repeat Transport Service. If two successive stages of an Oyster card used the
same bus route or rail station, the first is not linked to the next, regardless
of the direction of travel. Successive trips on the same service in opposite
directions indicate a return trip, while travel in the same direction (on the
same service) still indicates that a trip-generating activity was performed
(otherwise the passenger would not have alighted the vehicle).
Since Oyster bus records denote the route, this test is applied by simply comparing the route numbers of the current and subsequent transactions. For rail transactions, the station IDs of the Oyster records are compared. If they match, the current record is considered unlinked.
Subsequent origin inference. If the record following the current transaction
represents a bus stage without an inferred boarding location, it cannot be
compared to the current record during the temporal and spatial tests. If
a boarding location was not inferred for the following bus stage, the current stage’s interchange status cannot be inferred.
4.3.2
Temporal Conditions
Interchange Time. Since it is assumed that trip-generating activities are likely
to have longer durations than interchanges, a maximum interchange time
is imposed. As with Chu and Chapleau (2008), this limit is calculated as a
function of distance, but rather than adding a five minute buffer this algorithm sets a lower limit to the maximum interchange time, as follows:
[
]
d
tint = max tmin, —
Rmin
(4.1)
where
tint is the maximum interchange time,
tmin is the minimum interchange-time allowance,
d is the interchange distance: the Euclidean distance between
the current stage’s alighting or exit location and the following
stage’s boarding or entry location,
Rmin is the user-specified minimum walk speed,
The minimum interchange-time allowance is a user-defined parameter
that provides a small buffer when short interchange distances would oth68
erwise result in unreasonably short interchange-time thresholds. For example, if using a minimum walk speed of 3,000 meters per hour, two
intersecting bus routes with a pair of stops 20 meters apart would otherwise allow only 24 seconds in which to transfer. The minimum interchange-time allowance parameter allows for errors between Oyster and
iBus timestamps, while providing a small buffer for non-trip-generating
activities such as reloading an Oyster card.
If the next record represents a rail stage, and if the time between the
current stage’s exit or alighting time (t1) and the following stage’s stationentry time (t2) is greater than the maximum interchange time (tint), it is
assumed that the cardholder was engaged in an activity and the current
stage is considered not linked to the next. If the following record is a
bus stage, however, this test is applied and its result temporarily recorded,
but the current record’s interchange status is not affected, since the time
between the two stages might include some amount of bus wait time. In
such cases, the result of this test will be taken into account during the two
remaining temporal tests.
Maximum Bus Wait Time. This test is only applied if the following record
represents a bus stage and if the time between stages was greater than the
maximum interchange time (tint) in the previous test. If both conditions are
met, it is assumed that the rider either waited at the bus stop or was engaged
in a trip-generating activity before boarding.7 It is assumed that there is some
upper limit to the amount of time that someone will spend waiting for a bus,
regardless of the bus’s headway. The estimated time between the passenger’s
arrival at the stop and her subsequent boarding is therefore calculated as
t2 − (t1 + tint), and if the result is greater than the user-specified maximum
bus wait time parameter, the current stage is considered not linked to the
next.8
7 It is also possible that the rider boarded the bus immediately upon arrival at the stop, but
for consistency, riders are given the same time allowance to walk to a bus stop as they are
to walk to a rail station.
8 tint is used in the maixmum-bus-wait-time test for the same reasons as in the interchangetime test: short interchange distances are guaranteed a minimum time allowance to account
for the difference between Oyster and iBus times, or to allow brief, non-trip-generating
activities.
69
Observed Bus Headway. This test is only applied if the current record
passed the maximum interchange test. In other words, the following
transaction represents a bus stage and the cardholder boarded the bus
after the allotted maximum interchange time but before the maximum
bus wait time. It might therefore be assumed that the customer waited
at the stop before boarding the bus, but by searching the iBus data
the program can determine whether another bus assigned to the same
route served the stop while the customer was presumably waiting. If
the rider did not board the first bus that served the stop during this period, it is assumed that he was engaged in an activity rather than waiting.9 The iBus data are searched to determine whether another vehicle
served the route specified by the rider’s following trip during the interval
[t1 + tint, t2), and if so, the current stage is considered not linked to the next.
4.3.3
Spatial Conditions
Maximum Interchange Distance. Since the primary purpose of an interchange is to transition from one vehicle to another (and to ultimately
perform some activity at a subsequent destination), it follows that there
should be some limit to the distance between the current stage’s alighting
or exit location and the following stage’s boarding or entry location if
the transition is to be considered an interchange. The Euclidean distance
between the two points is compared to a user-specified maximum-interchange distance, and if the cardholder’s interchange difference is greater,
the current trip is considered not linked to the next.
Circuity. In addition to a maximum interchange distance, it is also assumed
that a multi-stage journey will not entail an overly circuitous path. If
the Euclidean distance traveled over two consecutive stages is sufficiently
greater than the Euclidean distance between the current stage’s origin and
the next stage’s destination, it is assumed that the disutility of the additional travel would outweigh the utility of the interchange.
9 The rider might have skipped the first bus because the vehicle was full or because the driver
did not stop (for example, if the driver knew that another bus was close behind). These
criteria are being considered for a future version of the algorithm but in the present version
it is assumed that, over the course of the entire day and network, it is more likely that a
skipped bus indicates an activity.
70
The algorithm tests for excessive circuity by first calculating the
Euclidean distances traveled during the current stage (dcur), the following stage (dnxt), between the current stage’s destination and the following stage’s origin (dint), and directly between the current stage’s
origin and the following stage’s destination (ddir). The sum of the
first three distances is considered the distance traveled, while the
third is considered the direct distance. The ratio of these distances,
(dcur + dint + dnxt) / ddir, is compared to the user-specified circuity ratio parameter, and if the observed ratio is greater than the parameter, the current
stage is considered not linked to the next.
The benefit of this metric rather than an angular cost is that, by taking distance into account rather than deviation, reverse travel is permitted
but penalized. Figure 4-2 shows two potential interchange locations for
the same origin and destination (assuming a zero interchange distance for
simplicity). Both paths have the same cumulative Euclidean distance, but
in the example to the right the destination is farther from the interchange
location than it is from the origin: the rider spent the first stage traveling
away from the second stage’s destination.
The ratio of the distance traveled to the direct distance defines an ellipse with the first stage’s origin and the second stage’s destination as its
foci. These elliptical bounds are more tolerant of lateral
travel than of
X
reverse travel, which is useful for defining interchanges. While a passenger might travel backwards for a short distance—for example, to take
a slower but more frequent local service before transferring to a faster
Ex. 1
limited-stop service—it
is assumed that riders are not likely to travel as far
D
O
backward as they are laterally.
X
X
Ex. 2
Ex. 1
O
D
O
D
Figure 4-2. Two potential interchange locations yielding equal cumulative Euclidean distances
X
71
Ex. 2
O
D
Ex. 2
D
O
O
D
.
.
.
.
.
.
.
.
.
.
Figure 4-3. Interchange boundaries for various circuity ratios.
Full-Journey Length. While the circuity test helps to prevent round trips
from being erroneously classified as single journeys, it only takes two successive journey stages into account. After all other tests have been applied,
full journeys are tentatively defined by grouping stages according to their
inferred link status. It is possible, however, that a set of journeys that includes a return journey (which by definition ends sufficiently close to the
journey’s starting point) passed the circuity test because no single transition was excessively circuitous, but the cumulative angular change over
two or more transitions resulted in a return journey. The journey-length
test therefore “unlinks” all stages in the journey (by reclassifying each
stage as not linked to the next) if the journey’s origin and its destination
were closer than the user-defined minimum journey length parameter.10
10 Two-stage journeys can be unlinked in this way but, in the case of journeys with three or
more stages (hence two or more interchanges), it is possible that only one of the tentative
interchanges must be unlinked in order to satisfy the journey-length condition, but it is
unclear which one. To infer interchanges as conservatively as possible, all of a journey’s
links are broken in such cases.
72
4.4
Results and Sensitivity Analysis
The interchange-inference algorithm was incorporated into the same Java
application that executes the origin- and destination-inference processes.
The software was tested on the same ten-weekday period used in Chapter
3,11 and typically completed in less than 20 minutes for each day, which
includes the time taken to execute the previous two processes. The application creates a copy of the Oyster data set enriched with the fields
inferred by the three processes. This section discusses the sensitivity of
the algorithm’s parameters and then presents the results of the ten-day
analysis.
4.4.1
Sensitivity Analysis
The results of the interchange-inference process are influenced by the values of the following six user-defined parameters, which allow the user to
select a balance between the accuracy and completeness of the results:
··
··
··
··
··
··
Minimum walk speed
Minimum interchange time allowance
Maximum interchange distance
Maximum bus wait time
Circuity factor
Minimum linked-journey distance
This section explores the sensitivity of the results to these parameters by
observing the distributions of the associated properties in the ten-day
Oyster data set.
Minimum Walk Speed. Figure 3-9 (page 53) illustrates the distribution
of out-of-system speeds recorded between consecutive journey stages,
where the latter transaction represented a rail stage (in order to exclude
bus wait time from the calculation). As discussed in section 3.3.3, the
speed is calculated as a Euclidean distance and therefore does not take the
cardholder’s path into account. The primary peak near 0 kilometers per
11 The 6th through 10th and 13th through 17th of June 2011.
73
hour likely represents activities while the secondary peak just below 5
kmph presumably indicates interchanges. Since the minimum walk speed
parameter sets a floor below which stages will be considered not linked,
it should be set low enough to retain people who may move slowly yet
high enough to exclude shorter trip-generating activities. The tests run in
this analysis used a value of three kilometers per hour, which appears to
provide a reasonable tradeoff between the two criteria and excludes 96.4
percent of the distribution, most of which represents obvious activities.
Decreasing the parameter to 1.9 kilometers per hour reduces the exclusion rate by one percent (to 95.4 percent), while raising it to 3.7 increases
the rate by one percent (to exclude 97.4 percent).
Minimum Interchange Time Allowance. When potential interchanges are
tested for excessive interchange time, this parameter provides a floor to
the criterion. It should be set in a way that enables the test to exclude
transitions of excessive duration while preventing the imposition of any
unreasonably short time limit.
Figure 4-4 shows the cumulative distributions of inter-stage time
for four stage-transition categories (all four permutations of bus and rail
stages, measured from the prior stage’s exit or inferred alighting time to
the latter stage’s entry or boarding time). Since most rail-to-rail interchanges occur behind the gate (and therefore within an Oyster journey
stage), the distribution of rail-to-rail times is likely to contain mostly ac%
Cumulative Frequency
%
%
%
%
Rail to Rail
Rail to Bus
Bus to Rail
Bus to Bus
%
%






Time (hours)



Figure 4-4. Time between cardholders’ successive journey stages, ten-day average.
74


tivities and few interchanges. The notable rise between 8 and 9.5 hours
likely represents workdays, which supports this assumption.
The three other distributions likely contain a mix of interchanges and
activities. Since bus-to-bus transitions can include return stages on the
same route, they are likely to contain a higher proportion of activities
than the remaining two distributions (the bus-to-bus distribution also includes bus wait times). The rail-to-bus distribution has a higher proportion of shorter transitions than the bus-to-bus distribution, likely due to
the absence of return trips on the same service.
The bus-to rail distribution, like rail-to-bus, is likely to contain a
higher proportion of interchanges than the first two distributions. However, bus-to-rail transitions exclude wait time, making this distribution a
more appropriate indicator of interchange times.
The 70 percent of bus-to-rail transitions that occur within five minutes likely indicate interchanges, as many bus routes in London stop very
close to rail stations. The tapering at approximately 12 minutes presumably indicates longer interchange distances, yet these can be accommodated by the program because an interchange time threshold is applied
as a function of distance. Setting a minimum allowance of five minutes
only affects those cases where the interchange occurs over a very short
distance, but ensures that the 70 percent of transitions that occur within
that time limit (and which presumably are mostly interchanges), are protected against being erroneously considered not linked. Decreasing this
parameter below five minutes can dramatically affect the inference rate
of short-distance interchanges, while doubling the setting to ten minutes
only changes the potential output from 70 to 75 percent.
75
Maximum Interchange Distance. Figure 3-10 (page 55) shows the distribution of distances between Oyster stages while Table 3-3 (page 55)
reveals the sensitivity of the maximum destination-inference distance.
The same distribution and sensitivity apply to the maximum interchange
distance, and a value of 750 meters is therefore applied to this parameter
as well.
Maximum Bus Wait Time. While Figure 4-4 shows the inter-stage times for
all Oyster transactions, Figure 4-5 combines bus-to-bus and rail-to-bus
times, after subtracting the estimated interchange time required for the
rider to arrive at the stop (assuming tentatively that the transition might
be an interchange). The dense but tapering cluster of times at the left of
the distribution fits the expected pattern of passenger wait times resulting
from the daily average of headways over the entire bus network, while
the long tail at the right should represent activities.12 While a threshold of
30 minutes might seem a reasonable cutoff for discerning waiting times
from activities, the observed headway test provides a more robust measure of how long a customer might actually have waited. For this reason, a maximum bus wait time of 45 minutes was applied in this research,
providing a backup in the presumably unlikely event of bus headways
exceeding that duration (for example, during service disruptions). This
conservative estimate excludes the 42 percent of the distribution that occurs to its right and which is very unlikely to contain a significant amount
of interchange activity, while allowing the observed-headway test to act
upon 58 percent of the distribution to its left. When set to 45 minutes, a
change in the maximum bus wait-time parameter of plus or minus three
minutes corresponds to a one percent change in the number of transactions eliminated by the maximum-bus-wait-time test.
12 The typical headways for London bus routes should result in a large number of customers
waiting for only a few minutes, which is likely represented by the high frequency of short
wait times in the histogram. Activities, by contrast, should be expected to exhibit a far
greater range of durations—for example, an hour for a meal or more than eight hours for
a work shift. Activities are therefore likely to be spread sparsely over the long tail of the
distribution.
76
,
Frequency
,
,
,
,
,
,





 45 

Implied Waiting Time (minutes)




Figure 4-5. Histogram of elapsed times between candidate bus-stop arrival times and subsequent bus boarding times.
77
Circuity Factor. Figure 4-6 shows the distribution of circuity ratios for all
potential interchanges that passed all previous tests (all binary and temporal tests, and the maximum interchange-distance test). More than half of
the distribution lies below 1.07, and the 95th percentile has a factor of 1.8.
The concentration of low circuity ratios is likely due in part to the
spatial arrangement of National Rail terminals in Central London. These
stations encircle the urban core, leading many commuters from outside
the city center to travel most of their journey by National Rail before
transferring to a TfL mode for the relatively short final stage of their trip.
For example, Figure 4-7 shows that a customer traveling by National Rail
(green) from Honor Oak Park to London Bridge, then transferring to the
Jubilee line (gray) and arriving at Bond Street, would travel within the
bounds of the 1.1 circuity factor shown on the map. Many National Rail
stations are much farther from the core than Honor Oak Park, leading to
even lower ratios.
Despite the dense concentration of circuity ratios below 1.1, the algorithm was applied with a factor of 1.7. Figure 4-8 shows a shorter journey, from London Fields National Rail station to Angel Underground
station. If this journey were made by traveling north to Hackney Downs
and transferring to the North London Line (orange) at Hackney Central,
it would require a circuity ratio of at least 1.5 (inner ellipse). If the rider
instead traveled south and made the interchange at Liverpool Street station, a circuity ratio of 1.7 (outer ellipse) would be needed. Although few
journeys have such high circuity factors, these journeys (and similarly circuitous bus journeys) are feasible commutes and should not be excluded.
,
Frequency
,
,
,
,

.
.
.
.
.
.
.
.
.
.
Circuity Ratio
.
.
.
.
.
.
Figure 4-6. Histogram of circuity ratios for potential interchanges (inter-stage transitions that
passed all previous tests).
78
A11
12
Waddon
Old Street
Wallington
Russell
Euston
Square
Square
Sutton
Carshalton
Goodge Street
Beeches
Tottenham
Court Road
Holborn
Covent
Garden
A
Piccadilly
Circus
2
Charing
Cross
Farringdon
Chancery
Lane
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E N T
Wes
P
Minimum Linked-Journey Distance. Figure 4-9 shows the distributions of
Euclidean distances between the start and end points all potential fulljourneys before applying the minimum journey-distance test. The small
cluster at the left of the distribution presumably represents sets of two or
more journeys that contain round trips, but which were not eliminated by
the previous tests. The transition between the two clusters appears to occur at 275 meters, but a more conservative limit of 400 was applied, since
common experience suggests that distances any shorter are unlikely to be
traveled on linked journeys.
,
Frequency
,

,
,
,
,
,



,
,
,
,
,
,
Distance (meters)
,
,
,
,
,
Figure 4-9. Histogram of potential full-journey distances.
4.4.2
Results
The interchange-inference process was performed on the ten-day Oyster sample using the parameters in the previous section, yielding the results shown in Table 4-3. Since bus origin and destination information
is required when inferring interchanges to, from, or between bus stages,
the inference rates of the origin- and destination-inference process affect
the interchange-inference rates of bus-to-bus, bus-to-rail, and rail-to-bus
transactions.
The number of Oyster cards recorded over the ten day period ranged
from 3.0 to 3.1 million per day, containing 9.5 to 10.1 million daily journey stages linked into 6.6 to 7.1 million daily journeys. The median journey duration was 24.5 minutes, with the distribution shown in Figure
4-10.
80
Table 4-3. Interchange-inference results: ten-day average.
Result
Count
Percentage
Current stage is tram
38,776
0.39%
Current boarding location not inferred
80,707
0.82%
Current boarding location inferred beyond
maximum error
156,580
1.58%
Next stage’s boarding location not inferred
55,620
0.56%
Next stage’s boarding location inferred beyond
maximum error
151,184
1.53%
Bus destination not inferred
140,248
1.42%
Bus destination inferred beyond maximum error
197,873
2.00%
90,409
0.91%
2,955,332
29.89%
886,344
8.96%
1,034,640
10.46%
Beyond maximum interchange distance
276,022
2.79%
Walk to rail stage slower than minimum walk
speed
334,111
3.38%
Maximum bus wait time exceeded
835,178
8.45%
Did not board first observed bus
363,741
3.68%
Too circuitous
109,395
1.11%
2,481
0.03%
911,397
9.22%
Subtotal, not linked to next
6,797,244
68.75%
Subtotal, assumed linked to next
2,139,578
21.73%
Total stages
9,848,219
Link status cannot be inferred because:
Next’s stage’s bus destination not inferred
Not linked to next because:
Final stage of day
Same bus route
Same rail station
Less than minimum journey length
Subtotal, interchange not inferred
81
,
Number of Full Journeys
,
,
,
,
,
,







Minutes





Figure 4-10. Histogram of inferred journey durations.
4.4.3
Comparison with the London Travel Demand Survey
Following Seaborn et al (2009), the results of the interchange-inference
process were compared to the London Travel Demand Survey (LTDS) of
the three-month period containing the ten-day Oyster study period.
A comparison of the number of daily journeys per passenger is shown
in Figure 4-11. Consistent with the findings by Seaborn et al, the survey
indicates a significantly higher proportion of passengers with two journeys per day than the Oyster data. This might be due to sampling error
(the LTDS sample is much smaller than the Oyster data set) or bias—for
example, respondents might be more likely to report their journeys to
and from work or school while underreporting recreational, unplanned,
or infrequent travel.
The difference between the two distributions might also indicate an
underestimation of interchanges by the algorithm. Table 4-3 shows that
9.2 percent of journey stages could not be inferred, and these stages are
treated as single-stage journeys (rather than discarding them and leaving
gaps in cardholders’ daily travel histories). Furthermore, the decision to
apply the interchange-inference criteria conservatively may have caused
some number of multi-stage journeys to be erroneously classified as multiple single-stage journeys, which might explain the larger number of
journeys per day in Figure 4-11, and, conversely, the smaller number of
stages per journey in Figure 4-12.
82
%
Percentage of Carhdolders
%
%
%
%
%
LTDS
Inferred
%
%
%
% %
%
%
%
%


%

%

Journeys per Day
%
%

% %
% %


Figure 4-11. Full journeys per passenger per day.
%
Percentage of Carhdolders
%
%
%
%
%
%
LTDS
Inferred
%
%
%
%
%
%
%

%

%

% %

Stages per Journey
% %
% %
% %



Figure 4-12. Stages per journey.
4.5
Summary
The algorithm presented in this chapter employs a range of temporal,
spatial, and logical assumptions about public transport interchanges and
applies a series of tests to infer whether each journey stage in a set of
roughly 9.8 million is linked to its cardholder’s following stage. Stages of
various public transport modes can then be joined to reconstruct the full
journeys and daily travel histories of roughly 3 million daily customers.
The tests for inferring interchanges were applied conservatively, and a
comparison to the results of London’s principle travel survey suggests that
the algorithm, when applied using the parameters described in this chapter, might be underreporting transfer activity. The interchange-inference
83
rate could also be increased by improving the inference rates of the originand destination-inference processes, which provide its inputs.
Further studies of the algorithm’s outputs could aid in the tuning of
its parameters. By mining multiple days worth of data, for example, activity types could be inferred, potentially revealing correlations between
a trip’s purpose and its spatial and temporal attributes. Different values for
the six interchange-inference parameters could also be applied to different
zones of the city, different service types, or during different times of day.
The implementation of the interchange-inference algorithm is discussed in Chapter 6, and multiple examples of its application are presented in Chapter 7.
84
5
Full-Journey Matrix Expansion
The methods described in previous chapters infer the details of passengers’
boarding, alighting, and interchange activity, which in turn yield information about passengers’ full journeys. Although these methods utilize
complete populations of AFC data, some AFC journeys are not properly recorded or cannot be inferred, while some riders pay their fares or provide
proof of payment using other media. The large market share of London’s
Oyster card and the high inference rates of the aforementioned methods
result in a very large sample of full passenger journeys (approximately 70
percent of all TfL passenger activity is inferred), but analyses of aggregate
travel behavior require that the sample values be scaled to estimate the
complete population of passenger activity on the TfL network.
This chapter describes a method for estimating expansion factors for
full passenger journeys during one (or more) user-defined time periods,
enabling the construction of a passenger flow matrix of travel activity
across multiple public transport modes. The method is then validated by
comparing the journeys’ constituent rail-stage flows to those estimated
using a traditional single-mode OD matrix of the Oyster rail network.
5.1
Problem Definition
Flows of passengers, vehicles, or goods are often described using origin–
destination (OD) matrices. By constructing a table in which each row representing an origin and each column a destination, the value in each cell
can be used to indicate the flow between a distinct OD pair. This is useful
85
for describing passenger flows between any two points during a given
time period, where the origins and destinations could be the entry and
exit stations on a closed rail network,1 the boarding and alighting stops on
a single bus route, or the geographic zones connected by a highway network. Full passenger journeys could be studied in a similar fashion—for
example, by counting the number of passengers who start at a given bus
stop and end at a given rail station, regardless of their intermediate transfer locations—but the inclusion of interchange locations in this work necessitates a different analytic framework.
Rather than estimating a scaling factor for each OD pair, this work
seeks to estimate a scaling factor for each full-journey itinerary, which
is defined in this context as a unique sequence of fare-transaction nodes
observed to have been visited by at least one passenger. The concept of
transaction nodes and itineraries can be illustrated using the hypothetical transit network shown in Figure 5-1 (referred to hereafter as the test
network).
TE 3
BUS
ROU
ROU
BUS
BUS
ROU
TE 1
TE 2
A
B
RA
IL L
IN E
C
Figure 5-1. The test transit network.
In the context of full-journey expansion, a transaction node is defined as a node on the network at which a rider can tap an Oyster card.
1 A network is considered closed if each passenger has one entry and one exit transaction but
no interchange transactions. The London rail network can be considered closed because
transfers are made behind the gate, yielding a single journey stage per system entry and exit.
86
Each transaction node is uniquely identified by either a station and movement (entry vs. exit) or a route and direction (inbound vs. outbound). For
example, the test network consists of the following twelve transaction
nodes (inbound is considered northbound in Figure 5-1):
Station A entry
Station B entry
Station C entry
Route 1 inbound
Route 2 inbound
Route 3 inbound
Station A exit
Station B exit
Station C exit
Route 1 outbound
Route 2 outbound
Route 3 outbound
This simple network yields 38 possible combinations of transaction nodes
that can be traversed on any passenger journey. For example, a common
itinerary might be station A entry to station C exit, while a less common
itinerary might be route 1 inbound to station A entry to station B exit to
route 2 inbound. It follows that every bus journey stage relates to a single
transaction node (identified by the route and direction), while each rail
journey stage consists of two nodes (an entry station and an exit station).
While this network of three routes and three stations yields a manageable 38 potential itineraries, the Oyster network contains over 1,300 station codes and over 800 routes. A very large number of potential itineraries can be defined, many entailing hundreds of interchanges and therefore
being extremely unlikely to be used by any passengers. It is for this reason
that the set of itineraries included in the analysis only includes those sequences of transaction nodes that have been observed to have been taken
by at least one passenger on the day being analyzed.
The estimation of full intermodal passenger-journey flows therefore
requires the estimation of an expansion factor for each full-journey itinerary observed in the sample. The approach taken is conceptually related
to some of the methods used to estimate traditional OD matrices, and is
presented after a review of the work upon which it builds.
87
5.2
5.2.1
Previous Research
Iterative Proportional Fitting on Closed Networks
The problem of expanding, or scaling, full passenger journeys is in some
ways similar to that of expanding OD flows on a single bus route or closed
rail network. In these cases a sample of passenger flows can be obtained or
inferred, and the flows can be scaled to match a set of control totals measured at each start and end point (or in the case of full journeys, at each
interchange point as well). The iterative proportional fitting (IPF) method
(Deming and Stephan 1940) is often used to solve the OD matrix expansion
problem for rail networks and bus routes (Ben Akiva et al 1985, Wilson
et al 2008, McCord et al 2010), and has also been applied to the London
Underground network (Gordillo 2006, Chan 2007).
If a rail origin–destination matrix is viewed as a contingency table,
with each row representing an origin and each column representing a destination, the IPF method attempts to estimate a scaling factor for each row
(origin) and another for each column (destination) that satisfy the following system of equations:
To,d = to,d · αo · βd
ΣT
= Co
ΣT
= Cd
d∈D
o∈O
o,d
o,d
∀ o,d
∀o
∀d
(5.1)
(5.2)
(5.3)
where
To,d is the estimated total passenger flow from origin station o to destination station d,
to,d is the observed sample passengers flow from origin station o to
destination station d,
αo is the estimated scaling factor for origin station o,
βd is the estimated scaling factor for destination station d,
Co is the control total for entries at origin station o,
Cd is the control total for exits at destination station d.
88
In other words, scaling factors must be chosen for each origin and each
destination such that the sample OD flows (collectively called the seed matrix) are scaled to satisfy each station’s entry and exit control totals.
In this way IPF yields a scaling factor for each OD pair, but each of these
scaling factors is the product of two other scaling factors: the entry and
exit factors αo and βd. When applied to the scaling of OD matrices from
Oyster data, this relationship necessitates the assumption that for each
OD pair, the proportion of travel successfully recorded with Oyster is a
function of the station of entry and the station of exit. This relationship
also provides the mechanism by which the IPF method solves the system
of equations.
Figure 5-2 depicts the IPF problem as a contingency table, using the
rail subsystem of the test network (stations A, B, and C) as an example.
Each row corresponds to an origin station; each column, a destination station; and each cell, an OD pair. Each sample flow (to,d) is multiplied by its
corresponding scaling factors (αo and βd), and the products are summed for
each row (∑o) and each column (∑d). Equations 5.1 through 5.3 show that
scaling factors must be chosen such that each row sum equals its corresponding origin control total and each column sum equals its corresponding destination control total.
A solution is obtained by first setting all scaling factors to 1, then repeatedly solving two of the three equations, alternating between solving for row factors (equations 5.1 and 5.2) and column factors (equations
5.1 and 5.3). In the example, this is achieved by setting all α to the corβA
βB
αA
0
tA,B tA,C ∑oA CoA
αB
tB,A
αC
tC,A tC,B
0
βC
tB,C ∑oB CoB
0
∑oC CoC
∑dA ∑dB ∑dC
CdA CdB CdC
Figure 5-2. IPF contingency table for the
test network shown in Figure 5-1.
89
responding quotient Co / ∑o, and then setting all β to the corresponding
quotient Cd / ∑d. Since the total estimated flow for each OD pair, To,d, is a
function of two scaling factors—one in the entry dimension and another
in the exit dimension—the adjustment of factors in one dimension can
satisfy that dimension’s control totals but typically upsets the balance in
the other dimension. With successive iterations, however, the differences
between the control totals and marginal totals (the row and column sums)
often converge toward zero.
Iterative proportional fitting, when convergent, yields the maximumlikelihood estimate for each OD pair’s scaling factor (αoβd) (Halberman 1974).
Ben Akiva et al (1985) compared IPF to other matrix-expansion methods,
including constrained generalized least squares (Björck 1996), constrained
maximum-likelihood, and an intervening opportunity model. They
found IPF to be preferable to the other methods due to its “computational
ease without loss of accuracy.”
Ben Akiva (1987) also noted that IPF will converge to a unique solution (a biproportional fit) if the seed matrix contains no zeros. If zeros do
exist, either because of infeasible combinations of origins and destinations (structural zeros, such as those OD pairs in the example that start and
end at the same station) or because the flow between a feasible OD pair
was not included in the sample (sampling zeros), the estimated total flow
for that OD pair will remain at zero. It follows that there will be no direct
relationship between that OD pair’s origin and destination scaling factors
(although they might influence each other indirectly through other OD
pairs), and that successive cycles might not converge. Pukelsheim (2012)
shows that in the case of non convergence, successive iterations tend to
oscillate between two accumulation points, one of which is approached
during the origin phase of the adjustment cycle; the other, during the
destination phase.
5.2.2
Applications of IPF on London’s Public Transport Network
Gordillo (2006) used IPF to build an OD matrix for the London Underground, using Oyster data to construct the seed matrix and station gateline data to derive the control totals. To correct for the bias of some
non-gated stations being undercounted, a method was devised which
supplements data from non-gated stations with data from manual counts
and from the RODS travel survey.
90
Gordillo’s OD matrix describes the London Underground’s travel activity for a full weekday, but Chan (2007) builds upon this work by devising a method for constructing a separate matrix for each of TfL’s six time
periods, as listed in Table 5-1:2
Table 5-1. London Underground weekday time periods
Time Period
Hours
Early morning
5:30–6:59 AM
AM peak
7:00–9:59 AM
Midday
10:00 AM–3:59 PM
PM peak
4:00–6:59 PM
Evening
7:00–9:59 PM
Late evening
10:00 PM–12:30 AM
If each time-period OD matrix were to contain only the passenger trips
that started and ended during the specified time period, passenger trips
that began during one time period and ended during another would be
excluded from the set of matrices. Chan therefore assigns trips to time
periods based on the time of the passenger’s entry tap, regardless of when
the rider tapped out of the system.3 The sample (the seed matrix) will
then include some portion of passenger trips that end during later time
periods, and which therefore contribute to the later periods’ control totals rather than those of the entry time period. Chan’s solution is to adjust
the stations’ exit control totals to incorporate some portion of the current period’s total and some portion of the subsequent period’s total. This
exit-adjustment methodology can be generalized to the adjustment of
rail-exit, rail-entry, or bus-boarding totals (all of which are required for
2 For a discussion of the homogeneity within time periods see Ji et al (2011).
3 Alternatively, trips could be assigned based on their end times, regardless of when the trip
started. This second approach might be more useful when analyzing the AM peak, when
commuters tend to choose various start times based on their desired (and often similar) arrival times. Conversely, the former approach might be more useful for the PM peak, when
passengers tend to enter the system at somewhat similar times (such as after work) but
whose exit times vary based on their travel times. For consistency, however it is conventional to choose one of the two approaches for all time periods.
91
the scaling of full-journey matrices4), spanning any number of arbitrarily
sized time periods, as follows:
tn,p,s
rn,p,s = —
tn,p,i
Σ
i∈P
Ĉn,A =
ΣΣr
p∈A s∈P
n,p+s,s
· Cn,p+s
∀ n ∈ N, p ∈ P, s ∈ P
(5.4)
∀n∈N
(5.5)
where
N is the set of transaction nodes (such as rail entry or exit stations)
on the network,
P is the set of recording periods: time periods for which controltotal data are collected for a given service day,
A is the analysis period: the time range for which the matrix is being constructed, which consists of a subset of the recording periods contained in P,
s is the span, or number of recording periods, between a given fare
transaction and the first transaction of the corresponding journey,
tn,p,s is the number of passengers in the seed matrix who tapped at
node n during recording period p, and whose journeys began s
recording periods before p,
rn,p,s is the ratio (or proportion), of all passengers in the seed matrix
who tapped at node n during recording period p, whose journeys
began s recording periods earlier,
Cn,p is the unadjusted recording-period control total: the number of
passengers counted at node n during recording period p,
Ĉn,A is the adjusted analysis-period control total: the estimated number of passengers who passed through node n, regardless of recording period, whose journeys began during analysis period A.
4 Since Chan’s OD matrices are defined by the time period of the entry transaction, only the
exit transaction count must be adjusted (the time period of the entry tap is by definition the
time period of the passenger trip). But since any full journey can include multiple station
entries, station exits, and bus boardings, all control totals must be adjustable to reflect the
time period of the first transaction in the passenger’s journey (for example, a station entry
might be part of an interchange that occurs in a later time period than the first transaction
in that journey).
92
Equation 5.4 illustrates that the timespan proportions (rn,p,s) are derived entirely from the seed matrix. It is assumed that the timespan proportions in
the sample are a reasonable proxy for those of the population, which are
unknown. Under this assumption, equation 5.5 applies the proportions to
the observed control totals, yielding the adjusted control totals.
Figure 5-3 illustrates the control-total adjustment process, using the
example of the exit counts at a London Underground station, with each
recording period corresponding to an hour. The table at the left contains
the timespan proportions, inferred from the Oyster sample. These are
multiplied by the unadjusted hourly totals, distributing them among the
Analysis
Period
Recording
Period
Timespan Proportions
Unadjusted
RecordingPeriod
Totals
(A)
(p )
( r n,p,s )
(C n,p )
A early a.m.
A a.m. peak
A midday
A p.m. peak
A eve.
A late eve.
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
s =0
s =1
s =2
s =3
0.0%
0.0%
0.0%
0.0%
0.0%
100.0%
66.7%
64.7%
59.4%
47.7%
48.7%
60.8%
62.1%
63.0%
58.9%
55.0%
62.2%
69.9%
59.1%
51.9%
55.3%
57.5%
55.3%
46.6%
24.7%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
33.3%
35.0%
40.1%
50.6%
49.9%
38.2%
37.1%
36.6%
40.7%
44.3%
37.0%
29.2%
40.4%
47.8%
44.4%
42.2%
44.7%
52.6%
75.3%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
0.5%
1.7%
1.5%
1.0%
0.4%
0.0%
0.4%
0.3%
0.9%
0.7%
0.4%
0.3%
0.0%
0.3%
0.0%
0.8%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.4%
0.4%
0.0%
0.3%
0.0%
0.2%
0.1%
0.0%
0.2%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Totals:
Adjusted Total
for
Period and Span
( r n,p,s · Cn,p )
Adjusted
RecordingPeriod
Total
Adjusted
AnalysisPeriod
Control
Total
(Ĉ n, A)
s =0 s =1 s =2 s =3
1
59
444
803
803
535
337
298
405
381
405
404
457
742
729
523
423
358
313
146
8566
1
39
287
477
383
260
205
185
255
224
223
251
319
379
289
243
198
146
36
4840
20
156
322
407
267
129
111
148
155
179
149
134
300
348
232
178
160
165
110
0
3669
1
4
14
8
3
1
0
1
1
3
3
3
2
0
1
0
3
0
0
0
49
0
0
0
0
1
2
0
1
0
1
1
0
1
0
0
0
0
0
0
0
7
22
199
623
891
654
392
315
336
412
408
376
388
623
787
612
468
406
363
256
36
221
2168
2293
1798
1486
655
8566
Figure 5-3. An example of the control-total adjustment process, using exit counts from a London Underground station.
93
four observed spans, as shown in the table at the right. The components
of each span/period count are then summed diagonally to yield the adjusted hourly totals, which are in turn aggregated by analysis time period.
The diagonal summation in the figure is reflected in equation 5.5, where
the hour dimension is incremented by p + s.
Chan also adapted the IPF procedure to ensure that all scaling factors
for each OD pair were no less than 1.0, since Oyster transactions are a
subset of a station’s fare activity and therefore cannot be greater than the
station’s control total. After adjusting the control totals, Chan constructs
OD matrices by applying Gordillo’s method to each time period, substituting Ĉn,P for Cn in equations 5.2 and 5.3 and adding the constraints αo ≥ 1
and βd ≥ 1. (A more robust method for applying these constraints to IPF is
presented in sections 5.4.1 and 5.5.1.)
5.3
Input Data
Like the IPF method, the full-journey expansion process requires a set of
sample data and control totals, as follows:
Origin-, destination-, and interchange-inferred Oyster data. After origins, destinations, and interchanges (ODX) have been inferred for Oyster transactions using the methods described in chapters 3 and 4, the Oyster records
can be used to construct a seed matrix of full passenger journeys, which
can in turn be scaled to match a set of control totals. The ODX-inferred
Oyster data only include those journey stages that were successfully processed by the ODX-inference tools: any data that were discarded during
any of the three inference processes will not be represented in the seed
data and must be accounted for by the scaling process. Additionally, any
Oyster trips that were only partially recorded, or any travel that was made
without an Oyster card, will not have been included in the seed matrix.
Because the seed matrix is an approximately 75 percent sample of TfL
travel, the probability of it containing a significant number of sampling
zeros is very low.
Rail Transaction Totals. Transaction counts from electronic station gates,
or gatelines, are available for most gated stations that accept Oyster cards.
94
Gateline counts are aggregated by date, hour, station, movement (either
entry or exit), and payment type. These totals include transactions made
with the gates’ Oyster readers as well as the magnetic-stripe readers used
to process paper tickets. There are some stations at which transaction totals are expected to undercount the number of actual transactions, such
as ungated stations at which passengers using valid travel passes are not
required to tap. Additionally, at the time of this writing, counts for some
stations (such as some of the new stations on the East London Line) are
not yet available.
Bus Farebox Counts. Data from bus fareboxes, or electronic ticket machines
(ETMs), include counts of each Oyster transaction, as well as of many nonOyster transactions such as paper tickets and visually inspected passes.
Bus operators are required to record the use of visually inspected passes
by pressing a button on the ETM but adherence to this rule varies among
drivers. Because of this variation, and because of the current difficulty in
obtaining disaggregate ETM data (as described in section 3.2.1), bus control
totals are obtained at the route level rather than at the stop level, with
the route direction being recorded as well. While stop-level counts may
be useful for route-level analyses, the use of route-level control totals
provides a reasonable level of aggregation for the scaling of intermodal
and system-wide travel activity: rider activity can be analyzed at the stop
level, but all stops will contribute to the ETM total of their route and direction. If stop-level data can be more easily obtained in the future, however,
the scaling processes described in this chapter can still be applied: it will
simply process a larger number of ODX combinations.
95
5.4
5.4.1
Methodology
Problem Definition Revisited
The problem of estimating expansion factors for full-journey itineraries
is illustrated in Figure 5-4. The entry gates at Station A and the exit gates
at Station B (from the test network in Figure 5-1) have different control
totals (ĈAo and ĈBd, estimated using the process generalized from Chan in
section 5.2.2), and the flows through these nodes comprise various fulljourney itineraries. The observed flow of each itinerary (t1 through t37)
must be scaled by some factor (α1 through α37) in order to estimate the
additional flow that was not observed or inferred from the Oyster data.
This additional flow, ∆Ao and ∆Bd, must be allocated to the itineraries that
constitute each node’s flow. In this example, itinerary scaling factors must
be chosen that satisfy the following two equations:
∆Ao = t1α1 + t2α2 + t3α3 + t4α4 + t5α5 + t17α17 + t18α18 + t19α19 + t20α20 + t21α21
∆Bd = t1α1 + t3α3 + t11α11 + t12α12 + t17α17 + t1α19 + t32α32 + t33α33 + t36α36 + t37α37
There are multiple solutions to each node’s equation. For example, all
of the unobserved flow entering Station A could be allocated to itinerary
1 by setting α1 to ∆Ao/t1 while setting all other scaling factors related to
the node (α1 through α5 and α17 through α3) to zero. The unobserved flow
could similarly be allocated exclusively to any of the other itineraries that
contribute to Station A's entry count. At the network level, however, the
problem is constrained by the relationships between transaction nodes.
Transaction nodes are related to each other through itineraries. In Figure 5-4, for example, itineraries 1, 3, 17, and 19 are common to both nodes.
Itinerary 1 starts at Station A and terminates at Station B, while itinerary
3 starts at Station A and then interchanges from Station B to Route 1 (see
figures 5-1 and 5-2). Most of the itineraries that constitute Station A's
entry flow do not pass through Station B's exit gates, but contribute to
the totals of other transaction nodes not shown in the figure. Since each
itinerary is given its own scaling factor, the factors for itineraries 1, 3, 17,
and 19 contribute to the unobserved flow of both nodes in the illustration
(∆Ao and ∆Bd). This relationship is illustrated for itinerary 1, for which the
96
t1α1
t1α1
t37
∆Ao
ĈAo
∆Bd
t1α1
t21
t20
t19
t18
t17
t36
t33
t32
t19
t17
ĈBd
t12
t5
t4
t3
t11
t2
t3
t1
t1
Station A entry
Station B exit
Figure 5-4. Example of estimated passenger flow on the test network.
sample flow (t1) is shown in a darker shade and the estimated non-sample
flow (t1α1) is delimited by a dashed line.
While the relationship between full-journey itineraries and transaction-node control totals constrains the problem, there is no guarantee that
a solution to the problem will be unique. An ideal scaling method would
therefore derive the most likely of all solutions to the problem, much like
IPF attempts to find the most likely solution to the OD-matrix-expansion
problem. The method proposed in this section therefore builds upon the
IPF approach, but modifies it to reflect the relationship between transaction nodes and full-journey itineraries.
97
5.4.2
Problem Formulation and Solution
The relationship between full-journey itineraries and transaction-node
control totals can be formulated as follows:
Σ t · B[n, i]
∆ n = Ĉn −
i∈I
i
Σ t · α · B[n, i] = Δ
i∈I
i
i
Ti = ti · (1 + αi)
n
∀n∈N
∀n∈N
∀i∈I
(5.6)
(5.7)
(5.8)
where
N is the set of all Oyster transaction nodes (bus routes inbound, bus
routes outbound, stations of entry, and stations of exit),
I is the set of all full-journey itineraries (unique sequences of Oyster transaction nodes inferred to have been taken by at least one
cardholder),
∆n is the difference between the control total and the sample total at
transaction node n,
Ĉn is the adjusted control total for transaction node n, as defined in
equations 5.4 and 5.5 (it is assumed that all variables correspond
to a single analysis period, A),
ti is the flow of cardholders inferred in the seed matrix to have
taken itinerary i,
B[n, i]is an incidence matrix—a binary matrix of ones and zeros—
indicating whether node n is traversed by itinerary i,
αi is the amount by which the sample flow for itinerary i will be
scaled,
Ti is the estimated total flow of passengers on itinerary i.
The purpose of equation 5.6 is to ensure that no itineraries are scaled
downward. Chan (2007) achieved a similar goal by imposing the constraint αoβd ≥ 1 in her IPF methodology (see section 5.2.2), which in the
full-journey expansion problem would correspond to a constraint of αi ≥
1. Applying such a constraint in an iteratively solved problem, however,
can inhibit convergence during any one phase of the cycle by forbidding
98
the selection of some factors that would otherwise (temporarily) satisfy
the control total, thereby propagating error to a subsequent phase.
By scaling the seed itinerary flows downward to the difference, ∆n,
rather than upward to the entire control total, Ĉn, the constraint αi ≥ 1
becomes unnecessary, since scaling factors now need only be greater than
or equal to zero (rather than one). The corresponding constraint αi ≥ 0
need not be applied because non-negativity is guaranteed by the definition of a scaling factor (αi) as the quotient of two non-negative numbers
(the control total and the sample total). After scaling factors are calculated
in this way, they are added back to the seed itinerary flow to derive the
total itinerary flow, as shown in equation 5.8.
Since the unobserved flows are unknown, the goal of the full-journey
expansion process is to find the most uniform scaling factors that satisfy
equation 5.7. If we assume that the best estimate is that all itineraries are
scaled as evenly as possible, we can seek to minimize the variance among
all scaling factors. This could be approached as a least-squares optimization problem, where the objective function—the scaling factor variance—
is minimized, subject to the satisfaction of equation 5.7. But with over
4,000 transaction nodes in the Oyster network and roughly 800,000 different itineraries observed on a typical weekday,5 the optimization problem requires a matrix of approximately 3.2 billion elements (the product
of the two values), making it too large to process on a typical computer.
A somewhat similar problem is solved by IPF, at least in the case of
traditional OD matrices. The convergence of errors between sample and
control totals is the direct result of repeatedly adjusting each scaling factor, which effectively distributes the potential error among all scaling factors, leading in many cases to the attainment of a maximum-likelihood
estimate (Halberman 1974). Since this meets the goals of achieving likely
estimates while satisfying control totals, a similar approach is taken when
solving the full-journey scaling problem.
In each phase of the IPF cycle, a scaling factor is estimated for each
node by dividing the desired flow (the control total) by the current flow
(the sample flow scaled by the expansion factors of the previous iteration).
A less direct approach must be taken for full journeys, since expansion
factors exist only in the itinerary dimension and control totals exist only
5 800,000 itineraries are typically observed when aggregating itineraries to the bus-route
level. When further disaggregated by bus stop, there are rougly 1.7 million daily itineraries.
99
in the transaction-node dimension (in IPF, factors and nodes exist in both
the origin dimension and the destination dimension).
Returning to the example in Figure 5-4, a scaling factor, α1, must
be estimated for the observed itinerary flow t1. If all nodes on the network are ignored except node A, we might assume that the distribution
of itineraries in the node’s unobserved flow is the same as the distribution of itineraries in the sample flow—that is, we would set α1 equal to
∆Ao /(ĈAo − ∆Ao). But it is clear from the example that the proportion of
a node’s sample flow assigned to an itinerary is not necessarily equal to
the proportion of the unobserved flow assigned to that itinerary. In this
case, t1 constitutes a greater proportion of the sample flow through node
A than through node B, while t1α1 constitutes a greater proportion of the
unobserved flow through node B than through node A. The approach
taken is therefore to choose an itinerary scaling factor that is the average
of the ratios of the itinerary's seed flow to the seed flows of its constituent
nodes. This problem is solved iteratively, and can be formulated as:
Mn =
Σ t · α · B[n, i]
i∈I
i
i
Δ
Σ—
M
α =α —
Σ1
∀n∈N
(5.9)
n
i
*
i
n∈i
n∈i
n
∀i∈I
(5.10)
where
Mn is the marginal transaction-node total: the non-Oyster flow
through node n, as calculated using the currently selected scaling
factor,
α*i is the itinerary scaling factor chosen during the previous iteration
of the problem,
and all other symbols are as defined earlier in this chapter.
Each Δn is calculated once, as shown in equation 5.6. All αi are then set to
1.0, and equations 5.9 and 5.10 are repeatedly solved until the marginal
totals converge upon the set of Δn.
Section 5.2.1 showed that convergence is reached in IPF by alternating
between the adjusting of entries and exits. The adjustment of any entry
100
scaling factor can directly affect any number of exit totals, but cannot
affect any other entry totals, and vice versa. All entries (rows) can therefore be adjusted simultaneously or in any order, and the process is then
repeated for all exits (columns). Since the full-journey problem contains
scaling factors only in the itinerary dimension and control totals only in
the node dimension, the adjustment of any itinerary scaling factor can affect the choice of scaling factors for other itineraries.
Because of this interaction between itinerary scaling factors, the order
in which factors are estimated can affect the results. If the scaling factors
in the example were calculated in order, itinerary 1 would be allocated
without being constrained by any other itinerary’s scaling factor, then
itinerary 3 would be allocated to some portion of ΔA not already allocated
to itinerary 1. Since there are approximately 4,000 transaction nodes on
the Oyster network but more than 800,000 itineraries, it is possible that
some number of control totals will be satisfied by the scaling of some
subset of their constituent itineraries before all of their itineraries are processed. This bias is eliminated by calculating scaling factors as described in
equations 5.9 and 5.10, but not applying the factors until the end of each
iteration, after all itineraries’ scaling factors have been calculated.
5.5
Example: Test Network
The methodology described in the previous section was first implemented in a spreadsheet program and applied to the test network (Figure
5-1). Unlike a real transport network, the total itinerary flows (which are
typically unknown) can be defined in advance, and the method’s outputs
can be validated against them. A rail-only OD matrix is then derived from
the full-journey matrix, and the results are tested against a traditional OD
matrix generated using IPF.
5.5.1
Validation
The full-journey scaling algorithm was applied to the test network, as
illustrated in Figure 5-5. The actual itinerary counts and Oyster penetration rates (the share of each itinerary count paid for or validated using
Oyster) are unknown in a real network, but were chosen in this example.
101
The actual itinerary flows are multiplied by the Oyster penetration rates
to derive the itinerary seed flows, which along with the control totals are
the independent variables in the algorithm.
Below the actual and seed itinerary counts in the figure are the estimated itinerary scaling factors, which are the only adjustable parameters
in the problem: the algorithm solves for these values. Beneath the scaling
factors is the incidence matrix (B in equations 5.6 and 5.7), which defines
the relationships between itineraries and transaction nodes by indicating
which of the twelve transaction nodes constitute each of the 37 observed
itineraries (there are 38 possible itineraries, but it is assumed that not all
were used by riders during the analysis period).
Comparing the full-journey scaling problem illustrated in Figure 5-5
to the IPF contingency table in Figure 5-2, it can be seen that IPF has control totals and scaling factors in both dimensions (the origin, or vertical
dimension, and the destination, or horizontal dimension). Since the fulljourney scaling problem contains flows in two or more dimensions (an
origin, a destination, and any number of intermediate interchange nodes),
Itinerary (i)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Actual itinerary counts (T)
200
176
22
29
38
84
27
123
28
16
87
16
134
21
74
58
16
21
7
13
5
Oyster penetration rate
50% 68% 91% 86% 79% 95% 67% 73% 82% 75% 86% 94% 82% 95% 81% 86% 75% 90% 86% 69% 80% 8
Itinerary seed counts (t)
100
120
20
25
30
80
18
90
23
12
75
15
110
20
60
50
12
19
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
6
*
9
*
4
*
Itinerary scaling factors (α) 0.68 0.46 0.59 0.39 0.43 0.26 0.29 0.26 0.22 0.26 0.36 0.31 0.16 0.19 0.07 0.39 0.59 0.41 0.52 0.39 0.35 0
Station A entry:
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
Station B entry:
0
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
Station C entry:
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
Station A exit:
0
0
0
0
0
1
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
Station B exit:
1
0
1
0
0
0
0
0
0
0
1
1
0
0
0
0
1
0
1
0
0
Station C exit:
0
1
0
1
1
0
0
1
1
1
0
0
0
0
0
0
0
1
0
1
1
Bus 1 inbound
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
1
1
1
Bus 1 outboond
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
1
0
0
0
0
0
Bus 2 inbound
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Bus 2 outbound
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
Bus 3 inbound
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
Bus 3 outbound
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
Figure 5-5. Formulation and results of the full-journey scaling process, as applied to the test
network.
102
0
all nodes are collapsed to a single dimension, with all three node types
(station entries, station exits, and bus boardings) listed on the vertical axis.
Since the itineraries correspond to the horizontal axis, scaling factors exist only in the horizontal dimension and control totals exist only in the
vertical. As with IPF, the scaling factors are initially set to zero. The problem is then iteratively solved as described in section 5.4.2.
The model was applied using various parameter settings in order to
test its robustness. If the iterative averaging process scales the itineraries
relatively evenly (within the constraints imposed by the control totals),
the results should be more accurate when there is less variance between
the itineraries’ penetration rates. Figure 5-6 illustrates the convergence of
the marginal totals (the estimated unobserved flow on each node) towards
the nodes’ control totals over 20 iterations of the algorithm. The first data
set, which contained Oyster penetration rates with a standard deviation
of 6 percent, converged to match all control totals within eight iterations.
The second data set, which had a standard deviation of 20 percent, con-
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
74
58
16
21
7
13
5
60
64
19
6
23
3
84
71
36
32
24
13
41
49
24
13
1% 86% 75% 90% 86% 69% 80% 83% 70% 84% 67% 70% 67% 83% 92% 97% 78% 75% 92% 73% 71% 67% 69%
60
50
12
19
*
*
*
*
6
*
9
*
4
*
50
45
16
*
*
*
4
*
16
*
2
*
70
65
35
25
18
12
30
35
16
*
*
*
*
*
*
*
*
*
9
*
.07 0.39 0.59 0.41 0.52 0.39 0.35 0.26 0.08 0.26 0.29 0.26 0.22 0.08 0.28 0.14 0.17 0.30 0.27 0.19 0.21 0.34 0.30
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
(M)
Marginal
(Ĉ)
Control
181 vs. 182
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
67 vs.
68
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
92 vs.
90
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
1
1
0
0
1
1
0
0
75 vs.
76
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
1
138 vs. 139
0
0
0
1
0
1
1
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
127 vs. 125
1
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
27 vs.
26
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
41 vs.
41
0
0
0
0
0
0
0
1
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
23 vs.
23
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
1
29 vs.
28
0
0
0
0
0
0
1
0
0
0
0
0
1
1
0
1
1
1
1
0
0
0
0
40 vs.
40
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
1
1
1
1
59 vs.
59
103

Station A entry
Error (marginal − control total)

Station A exit
Station B entry

Station B exit
Station C entry

Station C exit

Bus  inbound
-
Bus  inbound
Bus  outbound
Bus  outbound
Oyster penetration rate standard deviation: 6%
-
Bus  inbound
Bus  outbound
-
Standard deviation
Sum of squares
-











Iteration
















Error (marginal − control total)




-
-
Oyster penetration rate standard deviation: 20%
-
-











Iteration



Figure 5-6. Convergence of scaled transaction node flows toward control totals.
200
150
rmse = 3.6
Flow 100
Sample itinerary flow
Estimated itinerary flow
Actual itinerary flow
50
0
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Itinerary
150
rmse = 10.7
Flow 100
50
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Itinerary
Figure 5-7. Comparison of actual and estimated itinerary flows.
104
verged toward all control totals with the exception of two, which each
had errors of one rider.
Figure 5-7 compares the estimated itinerary flows to the actual flows
(which are unknown in a real network) and to the sample flows (which
are independent variables in the algorithm). On the data set with less variability, 34 of the 37 estimated flows provided closer estimates of the actual
flows than did the sample flows, and the root-mean-square error (RMSE)
between the actual and estimated totals was 3.6. The model with greater
deviation provided an equal or better estimate on 29 of the 37 itineraries,
with an RMSE of 10.7.
5.5.2
Comparison to Iterative Proportional Fitting
While the full-journey matrix-expansion process estimates scaling factors
for full-journey itineraries, these itineraries comprise individual journey
stages that can be compared to traditional single-mode OD matrices. For
example, the flow from Station A to Station B in the test network (see
Figure 5-4) can be derived by adding the scaled flows of all itineraries that
include that OD pair, or in this case:
Flow from A to B = t1(1 + α1) + t3(1 + α3) + t17(1 + α17) + t19(1 + α19)
By calculating flows for all OD pairs of the rail network in this way, a railonly OD matrix can be derived from the full-journey matrix.
The resultant rail OD matrix was compared to another rail OD matrix,
generated using IPF on the same data. The IPF algorithm was implemented
as described by Chan (2007) and Gordillo (2006), which were reviewed in
section 5.2. The only modification to the algorithm was to scale the OD
flows to the unallocated portion of the control totals rather than to the
entire control totals, as described in the full-journey estimation process
(section 5.4.2). The modified IPF algorithm is formulated as follows:
105
∆ o = Co −
∆ d = Cd −
Σt
d∈D
o,d
Σt
o∈O
o,d
Σt
· αo · βd = ∆ o
Σt
· αo · βd = ∆ d
o,d
d∈D
o,d
o∈O
To,d = to,d · (1+ αo · βd)
∀o
∀d
∀o
∀d
∀ o, d
(5.11)
(5.12)
(5.13)
(5.14)
(5.15)
where
∆ o is the difference between station o’s control-total entries and sampled entries,
∆ d is the difference between station d’s control-total exits and sampled exits,
and all other notation is as defined for equations 5.1 through 5.5.
Since the full-journey matrix expansion algorithm is more constrained
than the IPF method (both are constrained by the matching of node control totals while full-journey expansion adheres to the additional constraint of having to relate transaction nodes through itineraries), it should
be expected that there is more variation between the OD scaling factors
derived from the full-journey matrix than between the OD scaling factors
calculated through IPF. The small number of OD pairs on the test network
yields too small a sample to test this difference in variation (this is tested
in section 5.6.2 using the London network), but the comparison of each
OD pair’s scaling factors in Figure 5-8 shows that both approaches yield
similar scaling factors, with a greater variation in Oyster penetration rates
leading to greater variation between the results of the two algorithms.
106
120
IPF
ODX
100
Flow (Full -Joruney Scaling)
19.22385 19.75479
38.77615 38.24296
80
49.13286
48.25459
Oyster penetration rate
standard deviation: 6%
28.86714 29.74901
60
54.29441
54.74289
28.70559 28.25061
od pair
40
Jacked:
IPF
ODX
20
34.28263 39.03449
116.7174 112.2341
0
50.83356
44.69517
0
20
40
60
80
Flow (Iterative Proportional Fitting)
100
120
100
120
54.16644 60.64803
106.5798
110.7095
120
58.42019 53.31606
Flow (Full -Joruney Scaling)
100
Oyster penetration rate
standard deviation: 20%
80
60
40
20
0
0
20
40
60
80
Flow (Iterative Proportional Fitting)
Figure 5-8. Comparison of passenger flows for the test network
107
5.6
Application to the London Network
Following its validation on the test network in section 5.5, the algorithm
was implemented in a Java application (to be described in Chapter 6) and
applied to the London network using five consecutive weekdays of Oyster
and control-total data (October 17th–21st, 2011). Before calculating the
scaling factors, all control totals are automatically adjusted as described in
section 5.2.2. Figure 5-9 shows the results of this adjustment for Victoria
Underground station. The distribution of station exits is shifted to the
left, since some station exits correspond to journeys that started during
an earlier hour. For many stations the entry counts are not shifted as far
as the exit counts because station entries that are part of the first (or only)
stage in a journey define the journey’s start hour, thereby precluding any
offset. Victoria Underground Station exhibits a relatively large shift in
the distribution of entry counts because many of its customers transfer
from its interconnected National Rail station in the mornings.6
8000
7000
Raw entries
Passengers
6000
Adjusted entries
5000
Raw exits
4000
Adjusted exits
3000
2000
1000
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Hour
Figure 5-9. Hourly control totals for Victoria Underground Station, Wednesday, October 19,
2011.
6 The land uses surrounding Victoria Station are primarily commercial, yet the station’s entries peak during the morning and its exits peak in the afternoon. While these peaks contain some number of nearby residents and interchanges from bus, the peaks are largely due
to National Rail interchanges.
108
5.6.1
Results
After adjusting all entry and exit totals, the algorithm was applied to each
of the five daily data sets until no itinerary scaling factors were adjusted by
more than .001 percent (.00001) during a single iteration. For each of the
five days, this condition was satisfied after approximately 450 iterations
when generating a full-day OD matrix, and after approximately 2,500 iterations for peak-period matrices (with each iteration completing in less
than one second). All bus and rail control totals were matched to within
the ranges shown in Table 5-2.
Table 5-2. The degree to which the full-journey matrix expansion process satisfied the control
totals for all Oyster transaction nodes (five-day total), as measured by the ratio of each node's
scaled total to its control total.
Transaction node type
Min.
Max.
Bus boarding totals (by route and direction)
99.99999%
100.00001%
Station entry totals
99.99850%
100.00468%
Station exit totals
99.99537%
100.00153%
Unlike the test network, London’s actual itinerary flows are unknown
and cannot be compared with the estimated flows. Figure 5-10 shows the
average daily distribution of full-journey scaling factors, as well as the
ratios of bus and rail transaction node control totals to their sample totals.
The distribution is shown for the AM peak period and for the entire day.
Since each full-journey scaling factor (in the top histogram) is calculated
as an average of the scaling ratios of its constituent transaction nodes (the
middle and bottom histogram), it should be expected that the top distribution bears some similarity to the other two.
On average, the bus-route ratios are significantly higher than those
for rail stations. This is partly due to riders who board buses without
tapping an Oyster card, such as those who pay cash fares onboard or students whose passes are visually inspected by the operator (who can record the boarding to the ETM by pressing a button). The primary reason
for this discrepancy, however, is that approximately 24 percent of Oyster bus transactions are excluded from the seed matrix because they were
discarded during the origin- or destination-inference process. While rail
109
70000
60000
Full-Journey Itineraries
40000
30000
20000
Daily
 peak
10000
0
100%
105%
110%
115%
120%
125%
130%
135%
140%
145%
150%
155%
Full-Journey Scaling Factor
160%
165%
170%
175%
180%
185%
190%
195%
300
Bus Routes
250
Frequency
200
150
Bus: daily
Bus:  peak
100
50
0
100%
105%
110%
115%
120%
125%
130%
135%
140%
145%
150%
Control Total÷ Seed Total
155%
160%
165%
170%
175%
180%
185%
190%
300
250
Rail Stations
200
Frequency
Frequency
50000
150
Rail: daily
Rail:  peak
100
50
0
100%
105%
110%
115%
120%
125%
130%
135%
140%
145%
150%
Control Total÷ Seed Total
155%
160%
165%
170%
175%
180%
185%
190%
Figure 5-10. Histograms of scaling factors
scaling factors account for incomplete or non-Oyster activity, bus scaling factors account for non-Oyster activity plus the 24 percent of Oyster
activity that was discarded. The histograms also show that bus ratios are
lower during the AM peak period than throughout the entire day, suggesting that bus commuters are more likely to use Oyster cards than bus
riders in general.
Although the distribution of gateline-to-Oyster ratios is significantly
lower for rail stations, many of the stations with higher ratios have extremely high passenger flows. For example, four Underground stations—
Victoria, Oxford Circus, Liverpool Street, and London Bridge—account
for over ten percent of the total daily counts, and each station has a ratio
higher than 1.4. Three of these stations are adjacent to National Rail facilities, and the fourth, Oxford Circus, is a popular destination from the
other three. The higher ratios at these stations are likely due to commut110
200%
ers who live outside Central London and use non-Oyster point-to-point
season tickets to travel from National Rail to an Underground station
near their workplace.
The large number of 100% scaling factors in the rail ratios is due to
the fact that gateline data were not available for approximately 30 percent
of rail station codes.7 Most of these stations, however, belong to modes
that exhibit relatively low ridership in the seed matrix or that have only
recently been added to the Oyster network. Control totals should be obtained for these stations in the future, or their totals could be estimated
using other methods before being input to the program.
At present, the stations lacking control totals have a relatively minor
impact on the full-journey scaling process, as illustrated in Figure 5-11.
The X and Y axes compare the sampled and scaled flows of each full-journey itinerary, and the itineraries that are scaled to approximately 100%
(forming a 45-degree line from the chart's origin) can be seen to have
relatively small flows.
5,000
Scaled Flow (passengers per day)
4,000
3,000
2,000
Full-journey itinerary
1,000
0
0
1,000
2,000
3,000
4,000
5,000
Sample Flow (passengers per day)
Figure 5-11. Comparison of sample (or seed) flows to scaled flows, for all itineraries (five-day
average).
7 Gateline data were obtained for almost all Underground stations, but were not available for
several National Rail stations and some newer Overground stations (although these data
are recorded).
111
Validation Against Iterative Proportional Fitting on the London Network
As was done for the test network, a single-stage rail-only OD matrix was
derived from the full-journey matrix for the London network, and this
OD matrix was compared to another OD matrix generated using IPF with
the same Oyster and control-total data. Full-day and AM-peak OD matrices were generated for each of the five weekdays, and the five-day average
of each OD pair was used for comparison.
It was asserted in section 5.5.2 that the OD-pair scaling factors derived
from the full-journey scaling process should vary more than those obtained from IPF, since the full-journey algorithm is subject to more constraints. The distribution of OD-pair scaling factors from both methods
are shown in Figure 5-12: the series is sorted by scaling factor, with each
point on the X axis corresponding to a single OD pair. The distributions
are similar, but the full-journey curve exhibits greater variation, as it lies
below the IPF curve throughout the left side of the graph and lies above it
throughout the right. Since the curves in Figure 5-12 are sorted by scaling
factor, however, the OD pair at any point on one curve is unlikely to be the
same OD pair that occupies the same X position on the other curve.
Figure 5-13 compares both algorithms’ scaled flows for each OD pair.
While there is a generally linear relationship between the OD flows calculated using the two methods—presumably because both use roughly
similar techniques to scale the same sample data to the same control totals—the variations between the two is expected, since the full-journey
approach scales and constrains the problem at the itinerary level. The difference between the AM-peak and full-day scatter plots, however, is likely
due to the smaller sample size of the shorter analysis period. While the
300%
280%
260%
240%
Scaling Factor
5.6.2
220%
200%
Iterative Proportional Fitting
Full-journey scaling
180%
160%
140%
120%
100%
 Pair
Figure 5-12. Distributions of OD-pair scaling factors on London's rail network.
112
500
AM
Peak Period
Period
AM Peak
450
Scaled Passenger Flow (Full-JourneyScaling)
400
350
300
250
200
150
100
od Pair
50
0
0
50
100
150
200
250
300
350
400
450
500
Scaled Passenger Flow (Iterative Poportional Fitting)
500
All
Day
All Day
450
Scaled Passenger Flow (Full-JourneyScaling)
400
350
300
250
200
150
100
od Pair
50
0
0
50
100
150
200
250
300
350
400
450
500
Scaled Passenger Flow (Iterative Poportional Fitting)
Figure 5-13. Comparison of full-journey scaling and IPF by rail OD pair.
113
program converged upon the control totals in both cases, the AM peak
required roughly five times as many iterations to do so.
5.7
Summary
Although the full-journey matrix-expansion process has not yet been
tested against passenger surveys or other sources of empirical full-journey
information, its performance on a small test network with known values,
its matching of control totals on London’s transport network, and the
similarity between its constituent rail OD flows and those of a conventional OD matrix suggests that the algorithm is a reasonable method for
estimating full-journey scaling factors. The algorithm might also be applicable to similar problems, such as the estimation of highway flows by
using survey data to construct a seed matrix and traffic counts to serve as
control totals.
While this method provides a novel solution to the problem of scaling
full journey itineraries, transport agencies still perform many analyses at
the level of the unlinked passenger trip. By applying the full-journey expansion process to a transport network and storing the scaling factors or
the scaled route- or OD-level flows in a central database, single- or multistage analyses can be performed throughout the agency in a way that ensures compatibility between their outputs.
The technical implementation of the full-journey expansion algorithm is discussed in Chapter 6, and multiple applications of the process
are presented in Chapter 7.
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6
Implementation
One of the primary advantages of using automatically collected data as
a source of public-transport passenger information is the unprecedented
size of the sample. AFC, AVL, and aggregate transaction-count data enable
transport providers to observe the travel activity of most of their customers on any given day, but this data is only useful if it can be processed
efficiently. One of the goals of this thesis is to demonstrate the feasibility
of processing complete sets of London’s Oyster and iBus data on a daily
basis, and a significant portion of this research was devoted to the design
of a software application that achieves this goal.
This chapter describes the implementation of the origin-, destination,
and interchange-inference processes, as well as that of the full-journey
matrix-expansion algorithm. While exhaustive documentation of the
software’s code is beyond the scope of this thesis, the goal of this chapter—supplemented by the methodologies presented in chapters 3 through
5—is to provide enough information to guide the development of similar
implementations.1
6.1
Overview
The algorithms developed in this thesis were implemented using the Java
programming language, chosen for its flexibility, cross-platform compat1 Users of the code developed for this thesis, or those seeking additional technical documentation, should contact the author or the MIT Transit Research Group.
115
ibility, and succinctness. Rather than using a database to execute these
procedures, the program takes advantage of Java’s object-oriented features and stores collections of data objects in memory (and occasionally
caching them to disk), providing more control over the process and the
allocation of the system’s resources.
The application, consisting of approximately 10,000 lines of code,
was developed and tested on a consumer-grade PC to demonstrate that a
specialized server is not needed to execute the application,2 and care was
taken to minimize the memory footprint and tune the performance of
the process. A high-level overview of the process and its inputs and outputs is shown in Figure 6-1.
The classes of the software package are shown in Figure 6-2. The origin-, destination-, and interchange-inference processes are performed by
the OysterTransactionProcessor and NearestStopCalculator
classes, while full-journey matrix expansion is performed by the
ODXMatrixGenerator class. Journey-stage data are represented by the
Origin-inference process
Origin-inferred
Oyster file
Oys ter data file
iBus data file
Destination-inference process
Station data file
-inferred
Oyster file
Bus s top
data file
Interchange-inference process
-inferred
Oyster file
Station gateline
data file
Full-journey matrix-expansion process
Bus   
data file
Full-journey
scaling factors
Figure 6-1. Overview of processes, inputs, and outputs.
2 Unless otherwise noted, tests were performed on a PC with a 2.8 GHz Intel i7 Core processor
and 8 GB of RAM, running the Windows 7 operating system.
116
subclasses of both OysterTransaction and Stage: the former represents a relatively complete copy of the Oyster input data while the latter
contains a lightweight version having only those fields that are relevant
to the linking of journey stages. The OysterTransactionProcesser
dispatches searches for iBus data to an IBusManager, which contains a
hierarchical data structure of routes, vehicle trips, and stop events, while
a collection of OysterCard objects are used to store passengers’ daily
travel histories and perform various sorts, searches, and enumerations on
their constituent stages.
OysterTransactionProcessor
OysterTransactionProcessor
1
*
1
1
1
1
1
1
*
NearestStopCalculator
*
ODXMatrixGenerator OysterCard1
1
1
*
*
OysterCard
1
1
1
*
1
1
*
*
IBusRoute
IBusManager
1
Stage
*
1
1
*
*
Stage
BusStage
RailStage
1
BusStage
1
RailStage
*
*
1
IBusStopEvent
*
IBusTrip
Cardinality
1
OysterTransaction
*
*
OysterRailTransaction
BusStage
OysterTransaction
one
RailStage
Inheritance
many
IBusStopEvent
OysterBusTransaction
OysterBusTransaction
OysterCard
Stage
IBusRoute
IBusTrip
*
IBusStopEvent
*
*
IBusRoute
1
*
ODXMatr
1
1
OysterTransactionProcessor
1
IBusManager
IBusManager
IBusTrip
NearestS
1
1
1
1
1
superclass
*
subclass
OysterTransaction
OysterRailTransaction
OysterTramTransac
OysterTramTransaction
OysterBusTransaction
OysterRailTransaction
Figure 6-2. Class diagram of origin-, destination-, and interchange-inference package
117
Oyst
6.2
Process
To maximize compatibility with the program’s various data sources and
the systems which ultimately process its outputs, the application loads
data from text files and generates output in a similar format (although
the program could also be configured to interface directly with databases).
The program can be divided into five general processing steps, which are
described in the following subsections.
6.2.1
Step 1: Preprocessing and Nearest-Stop Calculation
The largest input to the program is the Oyster data set, which typically
consists of roughly 16 million daily records, but which contains several
fields that are not used by the program. Rather than storing these data in
memory, only the relevant fields are retained and the full records (including the information inferred by the program) are temporarily cached to
disk. All other data which must be matched with the Oyster records—
such as spatial information and AVL data—are therefore loaded into memory prior to the processing of Oyster information so that they can be
looked up as each record is read.
The first step in this process, as shown in Figure 6-3, is the loading of
all bus-stop IDs and their spatial coordinates. iBus data, typically comprising approximately 5 million records per weekday, are then loaded into
memory from a text file and are stored in the IBusManager data structure,
with each record from the input file being stored in an IBusStopEvent
and added to the appropriate parent IBusTrip and IBusRoute. Each
vehicle-trip number is unique within a route and day, thus allowing Oyster records to be uniquely associated with a trip using three fields (day,
route, and trip).
While the iBus data are being loaded and organized, an additional data
structure is built containing spatial information for each route pattern.
The top level of this second data structure consists of route patterns, each
uniquely identified by its route name and pattern number, separated by a
period (for example, “24.4882” denotes route 24, pattern 4882). The second level, within each pattern, contains the codes of all bus stops in that
pattern.
118
Bus s top
data file
Load bus stop data from file
iBus data file
iBus Manager
Load iBus data into iBus Manager
Nearest stop to stop
matrix
Generate matrix of nearest stops to all other
stops
Station data file
Load station data from file
Nearest stop to station
matrix
Generate matrix of nearest stops to stations
Oys ter data file
Load next Oyster record from file
Infer boarding location
[other]
[bus]
[rail or tram]
Oyster cards
Binary tem p file
Store stage data on related card and write
full record to binary file
[more records in file]
[all records read]
Sort each card's journey stages, incorporate OSI and
intermediate-validation information, discard unneeded records
Load next Oyster record from file
[bus]
Infer alighting time and location
[rail or tram]
[more records in binary file]
Infer interchange status
Binary tem p file
[all records read]
Link stages into journeys
[more records in binary file]
[all records read]
Look up journey information, write updated record to output file
odx-Inferred
Oys ter file
Figure 6-3. Origin-, destination-, and interchange-inference implementation.
119
The pattern collection is loaded in parallel with the stop-event collection: each stop event is denoted by its route and pattern, and redundant
stop events are ignored (since many bus vehicle trips serve the same pattern). Finally, spatial coordinates are loaded for all bus stops and rail stations, enabling the calculation of distances during this and other steps of
the destination- and interchange-inference algorithms.
The destination-inference process requires knowledge of the closest
stop on a bus pattern to a cardholder’s subsequent bus boarding or station
entry location. Rather than calculating the nearest stop for each of the
approximately six million daily bus alightings (which requires a distance
calculation for each stop in a route’s pattern), nearest stops are calculated
once for each combination of route and subsequent location, and are
stored for later reference. (The resultant “nearest-stop” matrices—one
for subsequent bus stops and another for subsequent rail stations—are
cached to disk and can be reused for multiple days’ analyses for as long as
the network and route patterns remain unchanged).3 Station information
is therefore loaded prior to the generation of the nearest-stop-to-station
matrix, and is retained in memory for use by other steps of the destination- and interchange-inference processes.
Efficient processing and memory allocation are top priorities due
to the large sets of data required. All four matrices are therefore implemented as two-dimensional primitive arrays, and the indices of the rows
and columns are implemented as one-dimensional arrays sorted by their
values to enable efficient access using the binary search algorithm (Knuth
1973).
6.2.2
Step 2: Origin Inference and Daily History Reconstruction
After iBus and spatial data are loaded into memory and initialized, the
program makes its first of three iterations over the Oyster data set to infer bus boarding locations while storing compact versions of the Oyster
records (stored as a subclass of Stage) to the appropriate OysterCard
object. For memory efficiency, the Oyster file is read one record at a time,
looking up each bus transaction’s boarding location in the iBus data structure and writing it to a binary output file (from where it will be read and
3 Bus stop and pattern information are typically updated every two weeks in iBus, or sooner
if changes are implemented.
120
processed during the following iteration).4 Tram boardings and station
exits are written to the output file without being processed, since their
origins are already known.
Oyster record types that are not relevant to the program—such as
top-ups, voids, unfinished station entries, and unstarted station exits—
are ignored, along with duplicate transactions made with TfL staff cards.
In many cases TfL staff use their Oyster cards to open station gates for exiting customers, often in response to equipment malfunctions, to correct
erroneous transactions, or to expedite passenger egress. All of these exit
transactions are assigned the entry time and location of the staff member’s previous entry tap and, moreover, the transactions are recorded to
the staff member’s card rather than the rider’s. For these reasons, any exit
transaction made on a staff card but not preceded by an entry transaction
is discarded (these discarded records should be accounted for in the control totals and therefore the scaling process).
Special handling is required for out-of-station interchanges (OSIs, described below) and intermediate-validation transactions. Intermediatevalidation and completed station-entry records are therefore temporarily
stored, along with the record types copied to the binary output file, as
Stage objects in the associated OysterCard’s collection.
After all records in the Oyster input file have been processed, each
OysterCard object sorts its child stage objects by transaction time to
construct a daily travel history. Intermediate-validation and OSI information are then added to the appropriate journey stages before performing
the second iteration over the Oyster data
OSIs are designated pairs of stations at which cardholders can tap out
of one station, quickly tap into the other, and pay a single fare for the
combined journey. When OSIs are recorded, the start time and location of
the stage before the interchange is written to its own exit record as well as
that of the stage after the interchange (recall from section 2.2.1 that entry
records contain entry data only, while exit records contain both entry and
exit data in order to charge the correct fare). This is appropriate for the
fare-collection application for which the Oyster system was designed: no
fare is charged for the prior stage, while the latter stage denotes the origin,
destination, and full fare of the combined journey. For the purpose of
4 The records are read from and written to memory buffers, to minimize the number of
harware I/O transactions.
121
full-journey travel analysis, however, it is important to accurately represent each physical stage in the cardholder’s journey. Therefore, the station
entry and exit records immediately following the OSI are compared, and
the start time and location are copied from the start record to the exit
record so that both journey stages associated with the OSI contain their
physical entry locations and times. The temporary Stage objects representing station entries are then discarded.
Following the handling of OSIs, the program incorporates intermediate-validation information, recorded by a dedicated type of Oyster record
which indicates the time and location of a cardholder transfer “behind
the gate.”5 Since no other type of behind-the-gate transfers are recorded
by the Oyster system, intermediate validations are not retained by the
software as fare transactions. However, they provide useful information
about the cardholder’s path and are therefore appended to two optional
fields in the related Oyster journey-stage record.
6.2.3
Step 3: Destination and Interchange Inference
Only after bus origins have been inferred and travel histories constructed
can interchanges or bus destinations be inferred. To ensure that all fields
in the Oyster input file are present in the output, the temporary binary
file written during the previous step is processed sequentially, and interchanges and bus alightings are inferred by looking up the associated travel
history in the collection of OysterCard objects. As in the first iteration,
the updated Oyster records are written to a buffered, temporary binary
file.
Bus alighting times and locations are inferred using the algorithm discussed in section 3.3.2. As each Oyster bus record is read, its corresponding journey stage object is retrieved from memory along with that of the
following stage (specified by the OysterCard object), and the specified
route, pattern, and target location data are used to retrieve the nearest
stop and its distance by looking up its value in the nearest-node matrices.
The inferred destination data are additionally recorded to the associated
BusStage object, in preparation for the inference of interchanges.
5 Intermediate validators enable customers to tap their cards at designated interchange stations to show that they transferred in a travel zone with a lower fare than that of another
potential interchange location.
122
After each bus destination is inferred, or rail record is read, its interchange status is inferred (tram journey stages can be linked to, and they
can be used to infer preceding bus destinations, but their interchange status cannot be inferred because their own destinations are unknown6).In
both the binary output file and the OysterCard data structure, alighting
times and locations are recorded, as well as the times and distances to the
cardholder’s next transaction and a flag indicating whether the stage was
linked to the following one. If a bus stage’s destination or any stage’s interchange status cannot be inferred, these values are replaced with one of
several error codes to indicate which test failed.
6.2.4
Step 4: Full-Journey Construction
The preceding step inferred whether each stage was linked to the next,
but stages cannot be linked into full journeys until the interchange status
of all of a card’s stages have been inferred.7 For this reason, journeys are
constructed after the second Oyster iteration, and a third and final iteration is required to append this journey information to the full Oyster records while writing them to the output file.
After the second Oyster iteration, all OysterCard objects are instructed to link their child Stage objects into journeys. By inspecting
the interchange-status flag of each stage, each can be assigned a journey
number, a stage number within the journey, and the number of stages
in the journey (for example, journey 2, stage 2 of 3). These three fields
enable the output data to be generated in a format similar to that of the
input data (with each row representing a journey stage rather than a full
journey), which in turn enables the full-journey matrix expansion software (or other analysis tools) to easily join records into journeys while
retaining the details of each stage.
After recording journey information to each Stage object, the second
binary file is read sequentially, the journey information is retrieved from
memory, and data from both are combined and written to the final Oys6 Tram transactions note the station where the fare was paid but riders do not tap their cards
upon alighting. Tram destinations could be inferred using vehicle-location data, but this
was not available.
7 Oyster data are not guaranteed to be stored in temporal order in the original input file or
the subsequent binary files.
123
ter output file. The file is identical in format to the original Oyster input
file, but contains the additional fields generated during the inference processes. The file can then be loaded into a database or other analysis tool, or
can be used to construct intermodal full-journey matrices.
6.2.5
Step 5: Full-journey Matrix Expansion
The full-journey matrix expansion process was implemented in the
ODXMatrixGenerator class, which can be called independently from
the OysterTransactionProcessor, provided that the latter has been
run and has generated the necessary Oyster output file. Following the
methodology presented in Chapter 5, the application generates an intermodal full-journey matrix for one or more time periods specified by the
user, adjusts the network’s control totals, and estimates expansion factors
to account for unobserved or uninferred passenger flows.
The first step is the construction of the seed matrix, as illustrated in
Figure 6-4. The ODX (origin, destination, and interchange)-inferred Oyster file is parsed and the origin and destination stop or station codes of
each record are stored in a lightweight inner class representing a journey
stage, with each stage being loaded to a similarly lightweight object representing a full journey. After Oyster data are loaded, all journey objects
are inspected and a set of full-journey itinerary identifiers is constructed
by concatenating the identifiers of the transaction nodes that comprise
the journeys. Since expansion factors are calculated at the bus-route level,
but full-journey matrices are to be constructed at the stop level, two
sets of matrices and identifiers are generated. For example, the identifier _24.708/_24.796/755/540 would signify all customers who
boarded bus route 24 at stop 708, alighted at stop 796, transferred to station 755, and ended their journeys at station 540.8 The identifier for the
associated route-level itinerary would be _24/755/540.
The route- and stop-level seed matrices are implemented as four-dimensional integer arrays indexed by transaction node, recording period,
span, and direction (inbound vs. outbound, or entry vs. exit).9 The first
dimension of each array is then mapped to the itinerary identifiers, and all
8 Underscores denote bus transaction nodes, periods separate routes from stops, slashes separate transaction nodes, and tildes denote tram stations.
9 See section 5.2.2 for terminology
124
Load -inferred Oyster data from file
ODX-inferred Oys ter file
Generate seed matrix of full-journey itineraries
Calculate timespan proportions for stations and
stops
Full-journey seed matrix
Time span proportions
Control totals
Load station gateline counts
Gateline file
Load bus ETM counts
ETM file
Adjust control totals
Adjust scaling factors for all itineraries
[scaling factors not converged]
[scaling factors converged]
Write scaled flow information to file
Scaled full-journey m atrix
Figure 6-4. Full-journey matrix-expansion implementation.
journeys are inspected again to look up the appropriate elements in both
seed matrices and increment the appropriate count. The journey collection is then discarded and the memory allocated to it is cleared.
Once the seed matrices are constructed, timespan proportions are initialized by constructing a four-dimensional array of floating-point variables with the same indices as the route-level seed matrix. The route-level
seed matrix is then analyzed as described in Chapter 5 to calculate the
timespan-proportion values.
Gateline and ETM data are then loaded from text files, in which they
are aggregated by node, hour, and direction (inbound/outbound for bus,
entry/exit for rail).10 Each input record is multiplied by the appropriate timespan proportion and is added to the corresponding element of a
three-dimensional array, similarly indexed by node, hour, and direction.
10 TfL’s gateline and ETM data are provided as hourly counts, which relate to the recording
periods in this implementation.
125
Finally, the scaling factors are estimated by repeatedly performing the
calculations in equations 5.9 and 5.10. The application iterates over the
entire adjustment process until the greatest change in any node’s scaling
factor between two successive iterations is less than the user-defined convergence threshold parameter, or until the number of iterations exceeds the
maximum iterations parameter.
6.3
Results and Performance
The origin-, destination-, and interchange-inference processes, which are
executed in a single function call, typically complete within 20 minutes
on an eight-core, 2.8 gigahertz, eight-megabyte PC. TfL staff have run
the process in under eight minutes on a 32-core 2.9 gigahertz server with
256 gigabytes of RAM. The full-journey matrix-expansion process, which
includes the construction of seed matrices and the adjustment of control
totals, typically completes in less than ten minutes on the MIT machine
and three minutes on a TfL server.
In addition to the text files containing the processed Oyster and fulljourney matrix information, the application generates a series of reports
that contain the performance statistics and inference rates displayed in
tables 3-1, 3-2, 3-4, 4-3, and 5-2.
6.4
Summary
The performance of the software described in this chapter demonstrates
the feasibility of applying the methodologies of this research on a daily
basis. By automating the application to run after hours, when all daily AFC,
AVL and transaction-count data have been transferred from their respective
servers, transport providers could obtain continuous system-wide fulljourney information. These data could be used to generate a variety of
automated reports, and would enable ex post analyses of any portion of
the network at any time.
126
7
Applications
The methods developed in this thesis can be applied to a range of analyses
on multiple public transport modes at various spatial and temporal scales.
TfL staff have used Oyster data to perform many analyses on the Underground, Overground, and National Rail networks (due to the requirement that passengers tap their cards both upon entering and exiting the
system), but the inference of origins and destinations on London’s buses
allows TfL’s highest-ridership mode to be included in these analyses.1 In
addition to observing travel on these multiple modes, the inference of
interchanges enables the observation of passengers’ full journeys, both
within and between modes.
This chapter demonstrates some of the applications of the methods
developed in this thesis, and describes its use by other researchers and TfL
staff. Analyses that rely only on the outputs of the OD-inference process
are presented first, followed by a summary of full-journey and longitudinal applications.
1 Bus ridership is higher than other public modes in terms of the number of Oyster journey
stages per day
127
7.1
Bus Applications
7.1.1
Boarding/Alighting/Flow Profiles and Route-Level OD Matrices
By extending the Oyster data set to include bus boardings and alightings,
the origin- and destination-inference process enables a number of analyses independently of the interchange-inference and full-journey expansion methods. Since passengers must tap their Oyster cards when boarding each bus, analyses can be conducted at the vehicle-trip level or can be
aggregated over many trips at the route level.
Enriched Oyster bus data can be used to generate passenger boarding, alighting, and flow profiles, as demonstrated in Figure 7-1 (showing
route 488 inbound, 7–10 AM). After loading the updated Oyster data into
a database, records can be aggregated by boarding and alighting locations,
with the cumulative difference between the two counts (boardings minus
alightings) revealing the passenger flow. The figure illustrates the total
flow on the route for five consecutive weekdays (9–13 May 2011) during
the AM peak, but individual vehicle loads can be calculated by simply aggregating the data by vehicle trip. Doing so could be useful for observing
the effects of crowding on rider behavior, dwell time, or service reliability.
In addition to aggregating ridership information by boardings and
alightings as in Figure 7-1, the data can be more finely aggregated by OD
pair. Figure 7-2 shows an OD matrix for the same route, direction, and
time period as shown in the graph, enabling a better understanding of the
relationships between the route’s boarding and alighting counts.
128
1500
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o (SB
treet
(NB)
igh S
Tav.
ley H
Priory
0
0
2
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/ Tesc
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w Sta
y-Bo
ley-b
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ley-b
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0
Brom
Bromley-by-Bow Tesco
Bromley-by-Bow Station (SB)
Bromley-by-Bow Station
Priory Tav. (NB) / Tesco (SB)
Bromley High Street
Bow Church
Bow Fairfield Road
Fairfield Road / Tredegar Road
Old Ford Hand & Flower
Old Ford Parnell Road
Old Ford Ruston Street
Jodrell Road / Wick Lane
Wansbeck Road / Monier Road
Rothbury Road / Wallis Road
Chapman Road / Wick Road
Kenworthy Road / Wick Road
Kenworthy Road / Hackney Hospital
Hackney Hospital
Brooksby’s Walk
Homerton Grove
Homerton High Street / Digby Road
Homerton High Street / Link Street
Urswick Road / Sutton Place
Lower Clapton Road / Coulton Road
Lower Clapton Road / Linscott Road
Lower Clapton Road / Downs Road
Brom
origin
Brom
ley-b
y-Bo
w Te
w Sta
sco
tion (S
B)
destination
7
2
9
2
–
–
–
–
–
–
–
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–
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20
24
0
30
20
4
–
–
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78
11
0
28
2
4
7
–
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52
Boar
din
Aligh gs
tings
Flow
4
2
2
0
0
7
7
–
–
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–
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7
0
15
0
0
17
13
4
–
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56
15
0
35
4
11
30
48
0
0
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7
0
7
2
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9
0
0
0
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–
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30
7
0
9
2
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0
7
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0
–
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2
0
56
2
0
7
15
0
0
0
0
0
–
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–
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83
2
0
9
0
2
7
15
11
9
2
17
11
7
–
–
–
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–
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91
9
2
11
2
0
2
9
0
7
7
2
2
0
0
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4
0
11
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4
13
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2
4
0
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0
7
–
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46
0
0
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2
2
4
28
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0
9
9
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93
4
0
13
4
2
17
26
2
20
4
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0
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0
9
43
7
–
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–
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156
2
0
4
0
0
2
2
0
0
2
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0
2
0
2
7
0
0
–
–
–
–
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–
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24
2
2
46
2
9
2
9
4
11
4
2
0
0
0
13
13
2
4
2
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128
0
7
35
4
0
9
11
4
13
2
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15
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15
0
4
–
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139
0
0
0
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7
2
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2
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13
4
13
4
7
17
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91
2
0
7
0
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9
0
2
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2
11
35
4
15
15
13
35
2
–
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159
4
0
7
2
0
4
0
2
2
0
2
2
2
0
15
17
4
35
7
15
24
0
0
–
–
–
146
0
0
9
0
0
2
2
0
2
0
0
0
0
0
24
28
17
20
11
7
13
4
0
9
–
–
148
0 130
0
15
4 384
4
56
0
46
4 146
9 224
0
30
11
89
4
37
7
39
0
20
4
17
2
7
22 135
26 206
13
52
22 124
4
43
15
61
24 113
9
15
7
7
2
11
2
2
0
–
195 2009
Figure 7-2.
Origin–destination matrix for Route 488 inbound, 7–10 AM
Scale to:
2009 2.17
6
0
0 17.4 2.17 19.5 78.2 52.1 21.7 56.5
143 30.4 36.9 82.5 91.2 52.1 45.6 93.4
156 23.9
128
139 91.2
129146
159
148
195
Bus Passenger Travel Time and Distance
7.1.2
In London, revenue is allocated to bus operating companies according
to the number of passenger miles traveled on each route, calculated as
the number of passengers recorded by the ticket machine multiplied by
that route’s average passenger journey-stage length. TfL has traditionally
used the Greater London Bus Passenger Survey (GLBPS) to determine average journey length, but analysts in the organization’s Fares and Ticketing
group have been testing the software developed in this thesis as a possible
replacement.2
Figure 7-3 plots the average journey distance observed through GLBPS
against those derived from the output of the OD-inference process. Each
6.0
5.5
5.0
Average distance, km ()
4.5
4.0
3.5
3.0
2 duties
2.5
3 duties
4 duties
2.0
5 duties
6 duties
1.5
7 duties
1.0
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
Average distance, km (-inferred Oyster)
Figure 7-3. Comparison of average journey-stage lengths: GLBPS vs. OD-inferred Oyster
2 Bus passenger miles were similarly inferred from AFC data by Lu and Reddy (2011).
130
6.0
point corresponds to a single bus route, and the data are classified by the
number of GLBPS duties—or surveyor shifts—that were undertaken on
each route. Routes with more GLBPS duties matched the Oyster data more
closely: Figure 7-4 shows the distributions of error between Oyster and
GLBPS, classified by number of duties (the lines extending from each class
indicate the minimum and maximum error, while the bottom, middle,
and top lines of the boxes indicate the first, second, and third quartile error values). For routes having zero or one GLBPS duties, TfL estimates an
average journey length by assigning the average of other routes having
the same fare zone (hence, an average of average journey lengths). Larger
sample sizes (in terms of numbers of GLBPS duties) appear to match the
Oyster average journey lengths more closely, as the median error for fiveduty routes is ten percent while 75 percent of routes having six or more
duties have less than a ten percent error.
Similarly to the calculation of bus journey-stage distances, durations
can be easily distilled from OD-inferred Oyster bus data. Schil (2012) uses
the outputs of the OD-inference process to calculate bus passengers’ invehicle travel times, which he then analyzes as an indicator of service reliability and expected travel time. By calculating the same metrics for rail
OD Inference Application: Passenger Journey Lengths
%
%
Percent change: Oyster vs. 
%
%
%
%
%
%
%
%
%
*
*




+
Number of  Duties per Bus Route
Figure 7-4. Comparison of average journey-stage lengths by bus route: GLBPS vs. OD-inferred
Oyster
*Routes with fewer than two GLBPS duties substitute the average journey length from other routes in the same fare zone.
34
131
stages, he is able to propose a single set of reliability metrics across all TfL
modes and for full intermodal journeys.
7.2
Cross-Modal and Full-Journey Applications
The applications discussed in the previous section were centered on buses,
although similar analyses have also been applied to London’s rail modes.
By inferring interchanges, however, travel can be studied across modes,
including the activity of passengers who span multiple modes in a single
journey.
The 7 million daily full passenger journeys revealed by the interchange-inference process typically follow approximately 1.7 million different full-journey itineraries. Full journey matrices are therefore cumbersome to present in their entirety, but can be aggregated to reveal the
flow to or from specific nodes or zones. For example, the origins of all
full journeys destined for Oxford Circus Underground station during an
AM peak period are shown in Figure 7-5.3 The busiest AM destination on
the Oyster rail network, Oxford Circus (highlighted in yellow, near the
city’s center) can be seen to attract riders from throughout Greater London. Origin node counts are color coded to denote the mode of the first
stage: all bus origins (red) therefore entail transfers to rail lines, since all
activity on the map terminated at an Underground station.
Rather than indicating origins and destinations at the level of individual bus stops or rail stations, these nodes can also be spatially aggregated by zones—such as postcodes, census areas, or traffic analysis zones.
Additionally, intermodal full-journey matrices can be aggregated by time,
enabling the visualization of the entire network’s travel activity.
Figure 7-6 captures a frame of an animated time-lapse full-journey
matrix (Gordon 2011). An optional function built into the matrix-expansion module tracks all passenger flows over the course of a day and interpolates each cardholder’s location every minute. Customers’ activities are
broadly inferred, as each passenger is either in a transit vehicle, between
transit trips (and therefore either performing an activity or transferring),
or at home (before their first or after their last journey of the day). Such
37–10 AM, Wednesday, 19 October 2011
132
Figure 7-5. Origins of full journeys ending at Oxford Circus station (base map: TfL).
133
Figure 7-6. Frame from a time-lapse animation of a full day’s inferred Oyster activity.
animations could be generated on demand for specific areas or times, enabling analysts to scan for patterns that might inspire more detailed adhoc analyses of the data.4
7.3
Observing Changes in Rider Behavior and Demand
Chapter 6 demonstrated that the algorithms developed in this thesis are
efficient enough to be applied every day on the full set of Oyster, iBus
and transaction-count data. A key benefit of acquiring and retaining full
populations of passenger data is the ability to track travel behavior over
time ( Jones et al 1998).
Muhs (2012) uses the outputs of the software developed in this thesis
to observe changes in travel behavior and service quality resulting from
the reopening of the East London Line (ELL). The former Underground
line was extended and integrated into the Overground network, and the
study observed riders’ responses to the new service as well as the impact
that it had on other transport services.
4 Examples of the animation can be viewed at www.jaygordon.net
134
134
Following Ng (2011), who used Oyster data to study changes in travel
time, route choice, and interchange location (using methods developed
by Seaborn [2009]), Muhs studied the effects of the line by observing
a panel of 54,000 Oyster cardholders who used the ELL in October of
2011 and whose cards were also active in April of 2010, before the line
opened. By conducting various analyses before and after the opening
of the line—such as riders’ mode choice, travel times, journey frequencies, and the analysis of boarding/alighting/flow profiles on parallel and
intersecting bus routes—the study was able to quantify many of the
project’s effects while testing the predictions of the ELL business case.
In addition to comprehensive studies such as those conducted by Ng
and Muhs, the analyses mentioned in the previous sections can be conducted on an ad-hoc basis to get a quick sense of changes in the transport system. Figure 7-7 illustrates the ridershed of the 205X, a special
Figure 7-7. First origins of day for riders of route 205x, Sunday, 28 August 2011.
135
bus service operated to serve the Notting Hill Carnival in the summer of
2011. By mapping the first daily boarding locations of the route’s riders, a
coarse estimate of their places of residence is obtained.
Similarly, boarding/alighting/flow profiles can be quickly generated
to compare travel behavior over time. The profile in Figure 7-8 was generated for route 488, which was extended in 2011 to serve Dalston Junction, the northern terminus of the East London Line. The chart shows
the changes in boardings, alightings, and flows, but the ad hoc generation
of a matching OD matrix reveals the OD flows that constitute this activity
(Figure 7-9).
7.4
Summary
The examples presented in this chapter illustrate just a few of the analyses
to which the methods of this thesis can be applied. The outputs of the
bus origin- and destination-inference processes alone enable several useful
types of analysis, and the application of full-journey expansion factors to
those data enable bus ridership profiles and OD matrices to be scaled in a
way that makes their outputs compatible with other studies.
The interchange-inference process enables the construction of fulljourney matrices, which can be used to measure and map various aspects
of ridership at a range of spatial and temporal resolutions. Ad-hoc analyses of special events, service closures,5 or system performance can be undertaken by staff using simple database tools, while large projects such
as the East London Line Extension, the 2012 Olympic and Paralympic
Games, and Crossrail can be studied in detail longitudinally in order to
assess their impacts and inform future projects.
5 AFC data have been used to study passenger behavior during maintenance closures on Chicago’s metro system (Mojica 2008).
136
1800
May boardings
October boardings
May alightings
October
alightings
May
alightings
May flow
October Flow
Flow change
Passengers
1500
1200
900
600
300
0
-300
Br Brom
om
ley ley-b
-by
y-B
Pri Bro -Bow ow T
ory ml
S
e
e
Ta y-b tatio sco
ver
n(
y
S
n ( -Bo
NB w S B)
)/
t
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S
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Fai
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ld Bow ow C eet
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t
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-600
Figure 7-8. Boarding/alighting/flow profile for route 488, before and after its extension to
Dalston Junction.
Boar
din
Aligh gs
t
Flow ings
3
0
0
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
3
1
0
3
0
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
4
h
Bow
8
0
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
8
Churc
0
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
0
Fairfi
eld R
oad
Fairfi
eld R
oad /
Tred
egar R
Old F
oad
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and &
Flow
Old F
er
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arnel
l Road
Old F
ord R
uston
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t
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ad / W
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ane
Wansb
eck R
oad /
Monie
Roth
r Road
bury
Road
/ Wal
lis Ro
Chap
ad
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Road
/ Wic
k Ro
Kenw
ad
orthy
Road
/ Wic
Kenw
k Ro
ad
orthy
Road
/ Hac
Hack
kney
ney H
Hosp
ital
ospital
Broo
ksby’s
Walk
Hom
erton
Grov
e
Hom
erton
High
Stree
t / Dig
Hom
erton
by R
oad
High
Stree
t / Lin
Ursw
k Stree
ick R
oad /
t
Sutto
n Pla
Lower
ce
Clapto
n Ro
ad
/ Cou
Lower
lton R
Clapto
oad
n Ro
ad / L
Lower
insco
tt Ro
Clapto
ad
n Ro
ad / D
Kenn
owns
inghal
Road
l Road
/
C
lapto
Clapto
n Way
n P on
d
Dow
ns Ro
ad / R
endle
Rend
sham
lesham
Road
Road
Recto
ry Ro
ad / A
mhurs
Shack
t Road
lewel
l Lan
e/C
ecilia
Shack
Road
lewel
l Lan
e/K
ingslan
Sand
ringh
d Hig
am R
h Stree
oad
t
Dalst
on K
ingslan
d Sta
tion
Dalst
on Ju
nctio
n
King
lsand
Road
/ Stam
ford
Road
) / Tes
treet
igh S
ley H
Brom
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
0
Bow
tion
w Sta
y-Bo
Priory
Taver
Brom
ley-b
n (NB
w Sta
y-Bo
y-Bo
Brom
ley-b
2009
May B
1915
OctA
3173
Brom
origin
Bromley-by-Bow Tesco
Bromley-by-Bow Station (SB)
Bromley-by-Bow Station
Priory Tavern (NB) / Tesco (SB)
Bromley High Street
Bow Church
Bow Fairfield Road
Fairfield Road / Tredegar Road
Old Ford Hand & Flower
Old Ford Parnell Road
Old Ford Ruston Street
Jodrell Road / Wick Lane
Wansbeck Road / Monier Road
Rothbury Road / Wallis Road
Chapman Road / Wick Road
Kenworthy Road / Wick Road
Kenworthy Road / Hackney Hospital
Hackney Hospital
Brooksby’s Walk
Homerton Grove
Homerton High Street / Digby Road
Homerton High Street / Link Street
Urswick Road / Sutton Place
Lower Clapton Road / Coulton Road
Lower Clapton Road / Linscott Road
Lower Clapton Road / Downs Road
Clapton Pond
Kenninghall Road / Clapton Way
Rendlesham Road
Downs Road / Rendlesham Road
Rectory Road / Amhurst Road
Shacklewell Lane / Cecilia Road
Shacklewell Lane / Kingsland High Street
Sandringham Road
Dalston Kingsland Station
Dalston Junction
Kinglsand Road / Stamford Road
ley-b
w Tes
co
tion (S
B)
co (SB
)
destination
13
6
21
17
10
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
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83 105 191 351 128
Figure 7-9. Origin–destination matrix for the extended Route 488 inbound, 7–10 AM
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8
Conclusion
The methods presented in this thesis have been shown to infer aspects of
public-transport passenger activity that are not directly recorded by automated data-collection systems. By providing results that are consistent
with those of manual recording techniques, this work provides a means of
obtaining similar information at a far lower cost, covering entire systems
on all service days rather than relying on small and infrequent samples.
This chapter summarizes the results and performance of these methods and assesses the degrees to which the research objectives were satisfied. Recommendations are then proposed for Transport for London or
others who adopt these methods, and suggestions are put forth for future
research that could build upon this work.
8.1
Summary and Findings
The bus origin-inference algorithm, previously validated against TfL’s
BODS survey for a sample of London bus routes (Wang et al 2011), was
refined and applied to complete daily sets of AFC and AVL data for ten
consecutive weekdays. Boarding locations were inferred for over 96 percent of the system’s 6.3 million daily bus transactions when applying a
maximum timestamp-matching error of ±5 minutes. Alighting locations
and times were inferred for 75.6 percent of bus transactions, using a set of
spatial and temporal parameters chosen after evaluating the distributions
of inter-stage distances and passenger speeds.
139
Interchange status was then inferred for 91.6 percent of Greater London’s 9.8 million daily journey stages (comprising the aforementioned 6.3
million bus stages plus several of the region’s rail modes). Approximately
30 percent of all journey stages were inferred to have been linked to their
following transactions, yielding approximately seven million daily full
passenger journeys. As with the destination-inference process, parameters
were chosen after exploring distributions of temporal and spatial properties of the data set. Results were compared to the London Travel Demand
Survey, revealing a difference in journey length (by number of stages)
which may indicate sampling bias in the survey or that the interchangeinference parameters were set too conservatively.
Processed Oyster data were used to construct seed matrices of full
passenger journeys, including travel spanning multiple public modes, for
five consecutive weekdays. Control totals from bus routes and rail stations
were adjusted to account for journeys that span multiple time periods,
and expansion factors for each full-journey itinerary were then estimated
for a set of both full-day and AM-peak-period matrices. When aggregated
by route or station, scaled passenger flows matched all control totals to
within .005 percent. Passenger flows were then derived for each itinerary’s constituent rail journey stages, and the scaled flows were validated
against a rail-stage matrix constructed using the well-established iterative
proportional fitting method.
All algorithms were implemented in a Java application, which typically performs the origin-, destination-, and interchange-inference processes on a complete daily set of data in less than 20 minutes on a consumer-grade computer. The outputs were then used to construct and
expand full-journey origin-interchange-destination matrices, typically in
less than 10 minutes. By appending origin, destination, and interchange
information to a copy of the original Oyster records and storing fulljourney expansion factors separately, information is stored at the resolution of the individual passenger transactions, enabling the analysis of
both granular and aggregate travel information.
In addition to being demonstrated for ad-hoc analyses of route- and
station-level ridership and service areas, the tools developed in this thesis have been applied to the visualization of daily system-wide passenger
activity, a rigorous evaluation of the effects of a recent capital project
(Muhs 2012), and the measurement of service quality and reliability (Schil
2012). TfL staff have been testing these tools as a potential replacement for
140
one or more travel surveys and are in the process of industrializing these
methods for use on a daily basis by multiple divisions of the organization.
8.2
Recommendations
This research demonstrates that complete populations of automatically
collected public-transport data can be processed efficiently enough to
support analysis on a daily basis, even for a large system such as London’s.
The long-term cost of developing the required software and allocating
approximately 30 minutes of computing time daily (on a server or merely
a workstation) is far less than that of conducting quarterly or annual surveys, which have much smaller sample sizes. It is therefore recommended
that transport agencies with AFC and AVL systems consider adopting these
methods as a replacement for many of their existing surveys, which
would enhance their analytic capabilities while making the majority of
their survey budgets available for targeted, supplemental studies.
While the methods in this thesis are largely generalizable to other
public transport systems, the implementation developed for this work
was designed specifically for TfL and can benefit from several additional
enhancements and validations. Since the rate of origin inference directly
affects the rates of destination and interchange inference, it is recommended that TfL consider correcting for erroneously defined vehicle
trips (as proposed by McCaig and Yip [2010]).1 Interchange inference similarly affects the reconstruction of full journeys and daily travel histories,
and the 91.6 percent inference rate can be improved—beyond the gains
from improved origin and destination inference—by obtaining AVL or
track data for trams. Doing so would enable the inference of tram alighting locations, and would enable that mode to be included in full-journey
analyses.
Attempts should be made to acquire control totals from stations for
which they are currently unavailable, such as some newer Overground
stations and a number of ungated National Rail stations. Where control
1 Some Oyster bus transactions occur after a vehicle trip’s final stop, suggesting that in some
cases iBus may have erroneously recorded a vehicle as serving a trip prior to the one that it
was actually serving.
141
totals cannot be obtained, TfL staff should develop a method for estimating default totals. These defaults could be applied to those stations during
the extraction of gateline data, or the software could be easily modified
to apply them at runtime.
Additional validation is recommended, especially against travel-diary
surveys such as LTDS. Respondents are now asked to provide their Oyster
card numbers voluntarily, and the matching of survey responses to Oyster activity could help find correlations between the spatial and temporal data provided by Oyster and the qualitative trip-purpose information
provided by the survey, thereby enabling the interchange-inference algorithm to more accurately distinguish interchanges from activities.
The analyses of spatial and temporal data conducted in this research—
such as the assessment of inter-stage distances and out-of-system speeds—
should be conducted at smaller scales, such as during specific time periods,
in different geographic areas, or on different types of transport services.
The parameters of the three inference processes could then be disaggregated if appropriate, allowing them to be set differently to account for the
variations observed across the system and over time.
This research was concerned primarily with the methodology and implementation of the above processes, and has only demonstrated a small
sample of its possible applications. The processes should be executed and
the results stored daily in a database to enable analysts throughout the
organization to perform ad-hoc queries, but reporting tools should also
be developed to satisfy the different analytic needs of various groups. For
example, bus service planners might benefit from an interface that dynamically constructs boarding/alighting/flow profiles or OD matrices for
user-specified routes and time frames, while bus travel speeds could be
aggregated by route segment and weighted by inferred passenger flows
in order to target the most critical areas for travel-time improvements.2
Similarly, capital planners might use a tool that maps the origins and destinations associated with user-specified interchange locations in order to
assess demand for new facilities and services.
It is recommended that TfL keep complete sets of processed data in
a live database for some reasonable duration, such as one or two years,
depending on the server infrastructure available. Older data should be
2 Sánchez-Martínez (2012) provides robust analysis tools for bus running time variability and
service quality, which could be combined with OD-inferred Oyster data for this purpose.
142
archived offline, but it is recommended that some subset is retained online, such as one week per month for more recent years and one week per
quarter for earlier years. Doing so would allow planners and analysts to
explore historical trends when considering service or infrastructure initiatives, and the selection of system-wide data over many points in time
could assist in the calibration of the organization’s various travel-demand
models.
Perhaps the most important determinant of the application and further development of this work will be the replacement of Oyster and
other AFC cards with contactless bank cards (RFID-enabled credit and debit
cards). TfL and other agencies are presently developing fare payment
systems which will allow customers to register bank cards for fare payment, then use the cards as they would AFC cards (Lau 2009, Brakewood
and Kocur 2012). Since fare transactions will be processed by banks rather
than transport providers, it is critically important that transit operators
contractually retain ownership of the transaction data. Failure to do so
would render the tools developed in this thesis unusable, or would put
agencies in the regrettable position of having to purchase their own data
from third parties.
8.3
Future Research
The methods developed in this thesis can enable a number of studies that
require large volumes of disaggregate ridership information, and the
methods themselves could be improved by synthesizing them with other
existing research.
The processing of Oyster journey stages enables the observation of
bus passengers’ in-vehicle travel time and speeds, travel paths, and interchange locations, but the ability to transfer between rail lines behind the
gate obscures customers’ paths while making in-vehicle travel time indistinguishable from platform wait time (or from in-system interchange, access, or egress times). Integration of this work with a path-choice model
(as demonstrated by Rahbee [2008]) would enable the inference of this
information, allowing analyses to be conducted at even finer levels of
granularity.
143
While the assumptions used to infer passenger alightings have been
validated against empirical data on several public transport systems (Barry
et al 2002 and 2009, Navick and Furth 2002, Zhao et al 2007, Wang et
al 2011), it should be expected that not all passengers alight buses at the
closest stop to the start of their next journey stage. Combining the nearest-stop rule with observed data from automatic passenger counters (APC)
could constrain the possible alighting locations for some bus journey
stages, resulting in a more accurate destination-inference algorithm.
The inference of both bus boardings and destinations enables the inference of vehicle load, which can in turn be used to enhance the interchange-inference algorithm. By estimating whether buses are full, or by
determining whether a bus is being closely followed by another, the algorithm could more accurately discern whether passengers were waiting for
buses or engaged in activities.
This research estimates spatial and temporal information about the
in-system and interchange portions of full passenger journeys, but access
and egress information could be inferred as well. By using the postcodes
of registered Oyster users (Ng 2011, Muhs 2012) to infer access or egress
distances (Alshalalfah and Shalaby 2007), a more complete model of passenger journeys can be constructed while providing empirical feedback
that could be used to calibrate the distance parameters of the destinationand interchange-inference processes.
Contactless bank cards, if their data are retained by transport agencies,
will necessitate changes to the destination- and interchange-inference
algorithms. Future research could address the benefits and challenges of
these new systems by distinguishing riders from cards (since a card could
be used to pay the fare of multiple riders) and by incorporating non-transit purchases if possible, to better discern journey purpose.
A wealth of information could be inferred by applying data mining
techniques to several days of processed Oyster records (Fayyad et al 1996,
Zhao 2009). Following Morency et al (2007) and Chu et al (2011), the daily
patterns of cardholders could be studied over time to infer broad activity types such as work, school, and recreation by analyzing the duration,
frequency, and regularity of visits to specific locations or zones. Travel
behavior variability could also be studied in this manner—for example,
by observing riders’ preferences between using the same service repeatedly or choosing from among services (perhaps serving the same corridor) based on arrival time, crowding, traffic congestion or even weather.
144
Observing a panel of individuals longitudinally could also reveal the patterns in which riders adapt to service changes or switch to new services.
Finally, the methods developed in this thesis can be applied to other
public transport systems. The software has been preliminarily tested with
data from the Massachusetts Bay Transportation Authority (MBTA), who
operate bus, subway, streetcar, and commuter-rail services in the Boston
metropolitan area. Such testing reveals the differences between the various inputs and parameters of the two systems. For example, MBTA subway
customers tap upon station entry only, necessitating a rail destinationinference process that will be similar in many ways to the process for
buses. Furthermore, the configuration and density of Boston’s transport
system (and underlying land use) is substantially different from that of
London, which is likely to require a different set of parameter values. By
applying this work to the MBTA and other transit networks, its methods
can be made more robust and adaptable, which might ultimately provide
a flexible means of enhancing the analytical capacity of transit agencies
throughout the world.
145
146
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