A Methodology for Identifying Potential Locations for Bus
Priority Treatments in the London Network
by
Farah J. Machlab
Bachelor of Engineering in Civil and Environmental Engineering
American University of Beirut, 2011
Submitted to the Department of Civil and Environmental Engineering
in partial fulfillment of the requirements for the degree of
Master of Science in Transportation
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2014
c Massachusetts Institute of Technology 2014. All rights reserved.
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Department of Civil and Environmental Engineering
May 23, 2014
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Haris N. Koutsopoulos
Research Associate of Civil and Environmental Engineering
Professor, KTH the Royal Institute of Technology
Thesis Supervisor
Certified by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mikel E. Murga
Research Associate of Civil and Environmental Engineering
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Heidi M. Nepf
Chair, Departmental Committee for Graduate Students
A Methodology for Identifying Potential Locations for Bus Priority
Treatments in the London Network
by
Farah J. Machlab
Submitted to the Department of Civil and Environmental Engineering
on May 23, 2014, in partial fulfillment of the
requirements for the degree of
Master of Science in Transportation
Abstract
Bus priority strategies provide preferential treatment to buses operating in mixed traffic.
This thesis aims at developing a methodology for identifying locations for potential bus
priority implementation, referred to as hot spots. While hot spots can occur at various
spatial levels, the research focuses on identifying hot spots at the intersection level.
Several measures are developed that describe the performance of bus routes through an
intersection. This research focused on measuring the running time variability, speed, and
delay of routes through an intersection. Using these route performance measures, two
intersection-level measures are defined. The first is an aggregate measure which weighs
individual route performance by the number of trips made through the intersection, and
the second is a normalized range measure that characterizes the amount of variation among
routes. A methodology for identifying hot spot intersections using a ranking approach is
proposed. Intersections that are ranked at the top in all aggregate measures are identified as
hot spots, as well as those that are top ranking in all normalized range measures. The final
list of hot spots also includes intersections that are top ranking in individual measures but
were not selected by the combined ranking. The methodology was applied to the London
network and the resulting list of intersection hot spots was compared with a list of hot spots
determined by Transport for London, London’s public transit agency.
This research also aimed at understanding the causes that influence overall route performance. A number of models were estimated using traffic and operating characteristics, in
addition to route and ridership attributes of a sample of routes in London. The results suggested that traffic and intersection delay are significant contributors to decreased speeds,
and bus lanes with lower bus occupancy are effective in increasing speeds.
Thesis Supervisor: Haris N. Koutsopoulos
Title: Research Associate of Civil and Environmental Engineering
Professor, KTH the Royal Institute of Technology
Thesis Supervisor: Mikel E. Murga
Title: Research Associate of Civil and Environmental Engineering
2
Acknowledgments
This thesis would not have been possible without the guidance, help, and support of so
many people.
Thank you to my advisors, Haris and Mikel, for their valuable insight and direction on this
research. Haris, thank you for your patience, for pushing me to do the best I can, and for
all your edits and comments on this thesis. Mikel, thank for the perspective you brought to
this work and for your advice. I would also like to thank Nigel, John, and Fred for making
the weekly research meetings one of the best parts of my MIT experience. Thank you Kris
and Kiley for making sure the department runs smoothly.
Thank you TfL for supporting this research. A sincere thank you to Alex Phillips, Keith
Gardner, John Barry, Rosa McShane, Andy Emmonds, Jonathan Turner and many others
at TfL, for their expertise and guidance.
Thank you to all my friends in the transit lab, for making every day interesting and fun.
Thank you Gabriel for all your help with Java and writing queries, your words of encouragement and for sharing my love of buses. Thank you Yiwen, Will, Winnie and Michel for
always offering your help. Steve, thank you being so great at TransCAD, for feeding me
delicious food, and for your positivity. Thank you Katie, for all the coffee, dance, and yoga
breaks, for making me laugh, but mostly for being a great friend.
To the MIT community and my Lebanese and Arab friends at MIT, thank you for making
me feel at home. Mohamad, thank you for being so easy to talk to. Thank you Esther, for
sharing your lab with me, and for being such a kind person.
To my best friends in the whole world, Nadine and Zeina, thank you for all your love and
support. You made me feel that nothing changed from our days in Beirut, that we’re still
in the same city instead of in three different countries. Thank you Dalia, Dalia, Lina,
Jad, Rabah, and Elia. You are my extended family and I’m so lucky to have you as my
friends. Thanks Hicham, for always being a source of entertainment. I know we’ll be friends
forever.
And finally, thank you to my parents and beautiful sisters, for everything that you have
done for me. You always knew I could do it, even when I didn’t think I can.
3
Contents
1 Introduction
11
1.1
Motivation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
1.2
Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
1.3
Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
1.4
Bus Priority Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
1.4.1
Running Way Treatments . . . . . . . . . . . . . . . . . . . . . . . .
15
1.4.2
Intersection Treatments . . . . . . . . . . . . . . . . . . . . . . . . .
16
1.4.3
Stop Treatments and Complementary Measures . . . . . . . . . . . .
18
Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
1.5
2 Literature Review
2.1
2.2
2.3
20
Methodologies for Identifying and Evaluating Potential Bus Priority Locations 20
2.1.1
Hot Spot Identification
. . . . . . . . . . . . . . . . . . . . . . . . .
20
2.1.2
Evaluation Methodologies . . . . . . . . . . . . . . . . . . . . . . . .
22
Factors Affecting Bus Performance . . . . . . . . . . . . . . . . . . . . . . .
23
2.2.1
Effect of Priority Measures on Bus Performance . . . . . . . . . . . .
24
2.2.2
Impact of Traffic on Bus Performance . . . . . . . . . . . . . . . . .
26
Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
3 Background on the London Buses Case Study
3.1
3.2
29
TfL and London Buses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
3.1.1
Bus Priority in London . . . . . . . . . . . . . . . . . . . . . . . . .
32
3.1.2
TfL’s Methodology for Identifying Hot Spots . . . . . . . . . . . . .
35
Available Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
4
3.2.1
iBus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
3.2.2
Traffic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
3.2.3
Accident Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
3.2.4
Spatial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
4 Measuring Intersection Performance for Bus Services
39
4.1
Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
4.2
Metrics of Bus Performance through Intersections . . . . . . . . . . . . . . .
40
4.2.1
Metrics at the Route Level . . . . . . . . . . . . . . . . . . . . . . .
40
4.2.2
Metrics at the Intersection Level . . . . . . . . . . . . . . . . . . . .
45
4.2.3
Other Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
46
Data Needs and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
4.3.1
Intersection Analysis Tool . . . . . . . . . . . . . . . . . . . . . . . .
48
Intersection Characteristics in London . . . . . . . . . . . . . . . . . . . . .
49
4.4.1
Aggregate Running Time Variability . . . . . . . . . . . . . . . . . .
52
4.4.2
Aggregate Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
4.4.3
Aggregate Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
55
4.4.4
Normalized Running Time Variability Range . . . . . . . . . . . . .
55
4.4.5
Normalized Delay Range . . . . . . . . . . . . . . . . . . . . . . . . .
55
4.4.6
Normalized Speed Range . . . . . . . . . . . . . . . . . . . . . . . .
60
4.4.7
Bus Accidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
4.4.8
Total Number of Buses
. . . . . . . . . . . . . . . . . . . . . . . . .
66
Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
4.3
4.4
4.5
5 Identification of Hot Spots in London
5.1
5.2
5.3
67
Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
5.1.1
Methods Considered for Identification of Intersection Hot Spots . . .
69
5.1.2
Ranking Methodology . . . . . . . . . . . . . . . . . . . . . . . . . .
73
Application of Ranking Methodology . . . . . . . . . . . . . . . . . . . . . .
77
5.2.1
Combined Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
5.2.2
Individual Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . .
84
5.2.3
Hot Spot Intersections and Prioritization . . . . . . . . . . . . . . .
84
Analysis of TfL Hot Spots . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
5
5.4
5.3.1
Characterizing TfL’s Hot Spots . . . . . . . . . . . . . . . . . . . . .
91
5.3.2
Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . .
104
6 Route Performance Models
6.1
6.2
6.3
6.4
107
Measuring Route Performance . . . . . . . . . . . . . . . . . . . . . . . . . .
107
6.1.1
Median Running Time, Running Time Variability, and Speed . . . .
108
6.1.2
Percent Lost Mileage . . . . . . . . . . . . . . . . . . . . . . . . . . .
110
Factors Affecting Route Performance . . . . . . . . . . . . . . . . . . . . . .
111
6.2.1
Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
113
6.2.2
Intersection Characteristics . . . . . . . . . . . . . . . . . . . . . . .
117
6.2.3
Route Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . .
119
6.2.4
Operator Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . .
121
6.2.5
Number of Bus Accidents . . . . . . . . . . . . . . . . . . . . . . . .
121
Model Specification and Analysis of Results . . . . . . . . . . . . . . . . . .
122
6.3.1
Models of Median Speed at the Direction Level . . . . . . . . . . . .
122
6.3.2
Models of Percent Lost Mileage due to Traffic at the Route Level . .
124
Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
126
7 Conclusion
7.1
7.2
129
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
129
7.1.1
Measuring Intersection Performance for Bus Services . . . . . . . . .
129
7.1.2
Identification of Hot Spots . . . . . . . . . . . . . . . . . . . . . . . .
131
7.1.3
Route Performance Models . . . . . . . . . . . . . . . . . . . . . . .
132
Limitations and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . .
133
A List of Hot Spots
136
B Model Descriptive Statistics
139
6
List of Figures
1-1 Degree of bus lane impacts (from Danaher (2010)) . . . . . . . . . . . . . .
16
3-1 Map of strategic roads in London (from Transport for London (2013a)) . .
31
3-2 London’s highway capacity for private motorized vehicles (from Transport
for London (2011a)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
32
3-3 London’s proposed cycle superhighways (from Transport for London (2013b)) 33
3-4 TfL potential bus priority locations . . . . . . . . . . . . . . . . . . . . . . .
36
4-1 Goswell Road and Old Street intersection . . . . . . . . . . . . . . . . . . .
49
4-2 Scatterplot of intersection measures . . . . . . . . . . . . . . . . . . . . . . .
50
4-3 Distribution of aggregate running time variability . . . . . . . . . . . . . . .
53
4-4 Distribution of aggregate delay . . . . . . . . . . . . . . . . . . . . . . . . .
54
4-5 Distribution of aggregate speed . . . . . . . . . . . . . . . . . . . . . . . . .
54
4-6 Quintile map of aggregate running time variability . . . . . . . . . . . . . .
56
4-7 Quintile map of aggregate delay . . . . . . . . . . . . . . . . . . . . . . . . .
57
4-8 Quintile map of aggregate speed . . . . . . . . . . . . . . . . . . . . . . . .
58
4-9 Distribution of normalized running time variability range . . . . . . . . . .
59
4-10 Distribution of normalized delay range . . . . . . . . . . . . . . . . . . . . .
59
4-11 Distribution of normalized speed range . . . . . . . . . . . . . . . . . . . . .
60
4-12 Quintile map of normalized running time variability range . . . . . . . . . .
61
4-13 Quintile map of normalized delay range . . . . . . . . . . . . . . . . . . . .
62
4-14 Quintile map of normalized speed range . . . . . . . . . . . . . . . . . . . .
63
4-15 Location of bus accidents in 2012 . . . . . . . . . . . . . . . . . . . . . . . .
64
4-16 Distribution of total number of buses per hour . . . . . . . . . . . . . . . .
64
4-17 Spatial distribution of total number of buses per hour . . . . . . . . . . . .
65
7
5-1 Intersection performance measures . . . . . . . . . . . . . . . . . . . . . . .
71
5-2 Distribution of number of times an intersection appears in the top 40 . . . .
74
5-3 Hot spot identification methodology . . . . . . . . . . . . . . . . . . . . . .
78
5-4 Combined ranking - aggregate measures . . . . . . . . . . . . . . . . . . . .
81
5-5 Combined ranking - normalized range measures . . . . . . . . . . . . . . . .
82
5-6 Aggregate RTV, delay, and speed - hot spot intersections . . . . . . . . . .
87
5-7 Normalized range of RTV, delay and speed - hot spot intersections . . . . .
88
5-8 Location of hot spot intersections . . . . . . . . . . . . . . . . . . . . . . . .
89
5-9 Total number of buses per hour at hot spot intersections . . . . . . . . . . .
90
5-10 Schematic of Bank Station intersection . . . . . . . . . . . . . . . . . . . . .
90
5-11 Distribution of aggregate RTV - TfL hot spot comparison . . . . . . . . . .
92
5-12 Distribution of aggregate delay - TfL hot spot comparison . . . . . . . . . .
92
5-13 Distribution of aggregate speed - TfL hot spot comparison . . . . . . . . . .
93
5-14 Distribution of normalized RTV range - TfL hot spot comparison . . . . . .
93
5-15 Distribution of normalized delay range - TfL hot spot comparison . . . . . .
94
5-16 Distribution of normalized speed range - TfL hot spot comparison . . . . .
94
5-17 Distribution of total number of buses per hour - TfL hot spot comparison .
95
5-18 Distribution of number of accidents per year - TfL hot spot comparison . .
95
5-19 Aggregate RTV, delay, and speed - TfL hot spots . . . . . . . . . . . . . . .
96
5-20 Normalized range of RTV, delay, and speed - TfL hot spots . . . . . . . . .
97
5-21 Schematic of Putney Bridge intersection . . . . . . . . . . . . . . . . . . . .
99
5-22 Schematic of Baker Street intersection . . . . . . . . . . . . . . . . . . . . .
100
5-23 Schematic of Kingsbury Circle intersection . . . . . . . . . . . . . . . . . . .
104
6-1 Distribution of median bus running time at direction level . . . . . . . . . .
109
6-2 Distribution of running time variability at direction level . . . . . . . . . . .
109
6-3 Distribution of median speed at direction level . . . . . . . . . . . . . . . .
110
6-4 Distribution of percent lost miles due to traffic . . . . . . . . . . . . . . . .
112
6-5 Median traffic travel time vs. median bus running time
. . . . . . . . . . .
115
6-6 Distribution of congestion index . . . . . . . . . . . . . . . . . . . . . . . . .
116
6-7 Traffic travel time CV vs. bus running time CV . . . . . . . . . . . . . . . .
117
6-8 Bus running time CV vs. percentage of bus lanes . . . . . . . . . . . . . . .
120
8
6-9 Distribution of average boardings per trip . . . . . . . . . . . . . . . . . . .
9
121
List of Tables
3.1
Breakdown of bus lane types in London . . . . . . . . . . . . . . . . . . . .
34
4.1
Correlation matrix of measures . . . . . . . . . . . . . . . . . . . . . . . . .
51
5.1
Distribution of measures for intersections appearing in three top 40 lists . .
75
5.2
Combined ranking using aggregate measures - top 10% . . . . . . . . . . . .
79
5.3
Combined ranking using aggregate measures - top 20% . . . . . . . . . . . .
80
5.4
Combined ranking using normalized range measures - top 10 % . . . . . . .
83
5.5
Number of times common intersections appear in six measures - top 10% .
83
5.6
Number of times common intersections appear in six measures - top 20% .
84
5.7
Top 10 intersections in aggregate measures . . . . . . . . . . . . . . . . . . .
85
5.8
Top 10 intersections in normalized range measures . . . . . . . . . . . . . .
86
5.9
Ranking method by which intersections in common with TfL were identified
98
5.10 Putney Bridge intersection performance measures . . . . . . . . . . . . . . .
99
5.11 Performance measures of routes through Putney Bridge intersection . . . .
100
5.12 Baker Street intersection performance measures . . . . . . . . . . . . . . . .
101
5.13 Performance measures of routes through Baker Street . . . . . . . . . . . .
102
5.14 Kingsbury Circle intersection performance measures . . . . . . . . . . . . .
105
5.15 Performance measures of routes through Kingsbury Circle intersection . . .
105
6.1
Descriptive statistics of median running time, variability, and speed . . . . .
110
6.2
Descriptive statistics of percent lost mileage due to traffic . . . . . . . . . .
111
6.3
Factors affecting route perfomance . . . . . . . . . . . . . . . . . . . . . . .
113
6.4
Estimation results of median speed model 1 . . . . . . . . . . . . . . . . . .
123
6.5
Estimation results of percent lost mileage due to traffic model 1 . . . . . . .
124
10
6.6
Estimation results of percent lost mileage due to traffic model 2 . . . . . . .
125
A.1 List of intersection hot spots - ranking methodology . . . . . . . . . . . . .
137
A.2 List of intersection hot spots - ranking methodology (cont.) . . . . . . . . .
138
B.1 Descriptive statistics of variables at the direction level . . . . . . . . . . . .
139
B.2 Descriptive statistics of variables at the route level . . . . . . . . . . . . . .
140
11
Chapter 1
Introduction
Public transportation agencies, specifically those operating bus services in large, dense
networks, have always been interested in bus priority. Bus priority, or more generally
transit priority, is the set of preferential treatments provided to transit vehicles operating
in mixed traffic on urban streets in order to deliver travel time savings and improved on-time
performance. Bus operations are subject to sources of uncertainty that are typically not
present in rail operations, making the potential benefits from implementing bus priority
strategies highly attractive. These strategies may have a number of objectives: first, to
reduce bus journey times, which contributes to increased capacity and/or a reduction in
the number of vehicles required to provide the service; second, to improve service reliability,
which translates to improved passenger experience and a more efficient use of resources; and
third, to reduce emissions caused by repeated stops at signals or waiting in traffic.
Decisions on where and which bus priority strategies to implement are important ones and
can have serious implications for both the transit agency and its customers. Funds dedicated to improving service reliability by implementing bus priority strategies are oftentimes
limited, so it is crucial to allocate them effectively, in ways that reduce costs for operators
and provide benefits to the most passengers. Many researchers have focused on the effects
of preferential treatment on bus service (Kimpel et al., 2005; Schramm et al., 2010; Diab
and El-Geneidy, 2013), but few studies have concentrated on the planning aspect of bus
priority implementation. This thesis aims at developing a methodology for identifying hot
spots in the bus network, to be ranked, categorized, and studied in more detail for bus
12
priority projects. In the context of this research, a hot spot is defined as having three
dimensions:
1. time within the day,
2. location in the bus network, and
3. measures that quantify the performance of bus route(s) in a certain time period and
location, whose values exceed some criteria set for selecting hot spots.
In recent years, advances in Automatic Data Collection (ADC) systems have provided transit agencies with inexpensive ways to measure and monitor system performance. Automatic
Vehicle Location (AVL) data, when used with other datasets, such as spatial information
and traffic data, can improve the understanding of bus performance in the network and aid
in the development of new analysis tools aimed at identifying opportunities for bus priority.
The methodology developed in this research is applied to the London bus network using
their AVL system (iBus) and several other databases.
1.1
Motivation
Developing a framework to identify areas of degradation in bus performance and opportunities for bus priority is crucial for service operations. Many components of bus operations are
stochastic; buses experience variability in running times and delays due to sources such as
traffic, signals, accidents, and variability in demand. As a result, performance deteriorates,
and mitigating these effects may require additional resources, in terms of vehicles, crew,
fuel and management costs. Pinpointing bus performance bottlenecks and implementing
priority treatments may be a more cost-effective solution. Additionally, developing such a
framework is an important tool in the operator’s arsenal and its value is only amplified by
the existence of high quality ADC systems, allowing the process to be applied frequently
and quickly.
Transport for London (TfL), the public agency responsible for all transportation services in
London, manages one of the largest bus networks in the world, with a fleet of about 8,500
vehicles, making it a suitable case study for this research (Transport for London, 2012a). Its
buses carry 2.3 billion passengers a year, and 21% of all journey stages in TfL’s system are
13
made by bus, the highest mode share percentage after car (Transport for London, 2013c).
In addition, London has a road network that is in transition and has set out a new vision
for its streets, which entails moving people and vehicles more efficiently, and transforming
the environment for more sustainable modes, as outlined in the Road Task Force (RTF)
report (Roads Task Force, 2013). To do so will mean reduced road space as new capacity
is created for walking and cycling putting increased pressure on bus services. Therefore,
identifying problematic areas for bus performance to aid in bus priority studies can help
mitigate the effects of some of these strategies, while still delivering the RTF’s vision.
1.2
Research Objectives
The overarching objective of this thesis is to develop a methodology for the identification
and ranking of bus hot spots for use in bus priority studies. This research mainly addresses
bus hot spots at the intersection level. Although it is designed specifically with the London
Buses context and available data in mind, the methodology aims to be applicable to many
other bus networks. The first goal of the research is to develop a set of metrics that describe
the performance of buses through an intersection using AVL data. The second is to develop
a methodology for the identification of intersection-level hot spots. The third goal is to
assess the factors that influence overall route performance, and explore the relationship
between route performance measures and these factors.
1.3
Research Approach
The first issue that needs to be addressed is the spatial boundary of a hot spot. In general,
this boundary can be defined at several levels: movement, intersection, corridor and route
levels. When buses travel through an intersection, only those performing a certain movement
may incur delay due to poor signal phasing or heavy traffic on conflicting movements.
When all buses passing through an intersection experience deterioration in performance
regardless of the movement made, then the entire intersection may be considered a hot
spot. This may be due to lack of capacity at the intersection because of geometry, heavy
traffic, or non-optimal signal settings.
14
Beyond the intersection, a road segment or corridor may be characterized as a hot spot
because it may either contain a series of hot spot intersections or because heavy traffic,
lane geometry, low speed limits, or road-side activities affect bus running times. A hot spot
at the route level, although extreme, may indicate that the route travels along corridors
and intersections identified as hot spots or the route has insufficient resources to perform
at the desired service level. This research recognizes that intersections are a bottleneck in
bus operations and looks mainly at hot spots at the intersection level.
The performance of buses through an intersection is described by a set of measures. Most
of these measures are developed using bus running times and describe the operational
performance of a set of routes. Specifically, they focus on describing running time variability,
delay and speed of the individual routes as they pass through the intersection. Intersection
metrics are developed to characterize two aspects of intersection performance. The first is
an aggregate measure which weighs individual route performance by the number of trips
made through the intersection to capture the average performance of an intersection. The
second measure characterizes the amount of variation among routes by defining a normalized
range measure, in order to capture discrepancies in performance among routes, which may
indicate movement-specific problems.
A methodology for identifying intersection hot spots is defined and applied to a sample of
London’s intersections, with the goal of identifying a subset of intersections that can be
studied in more detail to assess the feasibility of implementing bus priority measures. The
methodology uses the intersection measures developed in this research to rank intersections.
Ranking is done using both a combination of measures and individually. The results from
this approach are compared with the locations identified by TfL.
Models that explain typical bus speeds are useful for predicting the changes in speeds
following the implementation of bus priority measures. Several models are estimated that
aim at explaining speed as a function of important route attributes and characteristics of the
operating environment, such as traffic congestion. In addition, the relationship between the
percent lost mileage due to traffic, a route performance measure that TfL uses to identify
priority locations, and congestion is explored.
15
1.4
Bus Priority Treatments
There are several different forms of bus priority treatments that can be applied on streets.
They can be categorized generally as treatments relating to the running way, to intersections,
or to stops. The impact on bus operations will be determined by the extent of priority
treatment provided and the traffic conditions along the roadway. In addition, treatments
can be deployed at isolated locations where there is consistently high delay to buses, or as a
series of treatments along a corridor, which will have a larger impact on travel time savings
and reliability. The Transit Cooperative Research Program Synthesis 83 (Danaher, 2010)
presents a summary of bus and rail transit preferential treatments in mixed traffic, which
are described in detail in the following sections.
1.4.1
Running Way Treatments
Treatments to the running way can be either guided busways or more typically, bus lanes.
Guided busways are transit facilities where buses travel on a dedicated right-of-way for both
the running way and stations. Buses typically interact with general traffic at signalized intersections, with cross street vehicles and left-turning traffic on the busway street (or in
the case of London, right-turning traffic). Wide streets are necessary for the implementation of guided busways; these facilities require sufficient right-of-way to provide for station
platforms, left-turn lanes at signalized intersections if a protected left-turn phasing is to be
provided and passing lanes if the busway is to be used by more than one route. Therefore,
guided busways are usually warranted when there is a large number of vehicles in operation
and there is a desire for higher speeds.
Bus lanes are perhaps the most common type of preferential treatment implemented. These
are lanes provided along the roadway by widening or dedicating one or more existing general
traffic or parking lanes. Bus lanes may vary in terms of restrictions on use, location in the
roadway and flow of bus traffic and may be distinguished by specific markings, signage and
barriers from the general road space. They may be exclusive to buses, or restrictions could
allow for use by taxis, high-occupancy vehicles, bicycles and emergency vehicles. In some
cases, general traffic may also use the lanes for left- or right-turning movements. Bus lanes
may be in effect during peak hours only, or all day. They may be placed in the curb/parking
16
lane, middle lane, center lane or in the median. If the lanes are designated for buses only,
then buses may travel in the same direction as general traffic (concurrent-flow lane), in the
opposite direction (contraflow lane), or for a short time duration only needed to allow buses
to pass before changing back to a general traffic lane (intermittent or moving bus lane).
As with other priority treatments, bus lanes contribute to improved bus service reliability
and higher speeds and may even affect mode choice (see Figure 1-1 for exclusive bus lanes),
but are likely to have impact on general traffic, such as increased congestion, a reduction
in private vehicle capacity, or the elimination of curb lane parking. Therefore, bus lanes
should be implemented when there is a high frequency of service, traffic congestion on the
road is significant, and road space is available.
Figure 1-1: Degree of bus lane impacts (from Danaher (2010))
1.4.2
Intersection Treatments
Preferential treatments at the intersection level are usually transit signal priority (TSP) or
queue jump lanes.
TSP modifies traffic signal timing at intersections to accommodate buses operating in guided
busways, exclusive lanes, or in mixed traffic, while maintaining the coordination among all
other phases and the cycle length.
17
TSP can be either passive or active. Passive strategies rely on pre-timed alterations to
the signal timings to provide some degree of priority to buses. These modifications occur
whether or not a bus is present. Active strategies change the signal system after the bus
is detected as it approaches the intersection, and can be applied as either conditional,
unconditional, or real-time priority. Conditional priority grants priority only if a bus meets
some criteria, such as if the vehicle is behind schedule or carrying a certain number of
passengers, and therefore requires AVL and/or APC data. Unconditional priority, on the
other hand, provides priority to all buses that are detected as they approach the intersection.
Real-time priority takes into consideration both bus conditions and general traffic arrivals at
the intersection, and requires specialized traffic control equipment. Active priority strategies
can adjust the green phase of the approach serving the bus by either extending the green
phase for a bus arriving late at the intersection (green extension) or by advancing the green
time for buses waiting at the intersection (green recall/red truncation).
In addition to improving travel times, travel time variability and service reliability, TSP
has the added benefit of improving the person throughput of the intersection by benefiting
general traffic traveling with the bus; these impacts vary with the application of TSP (it
may be at an isolated intersection or along a corridor) and the extent to which the current
signal system has optimized operations. However, TSP may increase delays for traffic on
cross streets and the cost of implementation may be significant. In addition, TSP is typically
most effective in corridors with heavy traffic congestion, where the estimated reduction in
bus delay causes negligible change in general traffic delay, and with far-side stops so that
the bus activates the priority call and travels through the intersection before making a
stop.
Queue jump lanes are short lanes provided at intersections to allow for buses to bypass
general traffic. These lanes must be long enough to allow buses to access them without
being blocked by queued vehicles on the adjacent lane. Queue jumps can be provided with
or without signal priority. Queue jumps, when implemented in conjunction with TSP, may
provide some level of time savings that the bus would not otherwise receive, especially in
corridors where an exclusive bus lane is infeasible. However, the extent of these savings
may vary depending on the stop location at the intersection, the amount of right-turning
traffic, and whether a separate lane is provided for right turns.
18
1.4.3
Stop Treatments and Complementary Measures
Stop design can have a significant impact on bus travel times. Physical features such as
curb extensions can provide a type of priority for buses, but modifications to stop placement
and other complementary measures can also create some travel time savings.
Curb extensions involve extending the sidewalk area at the stop location into the street.
Buses do not have to exit the travel lane to serve passengers, therefore eliminating the time
spent waiting for a gap in the traffic stream in order to merge back into the through lane.
They can be applied at any type of stop yet often involve the removal of parking spaces
or loading zones, and can produce significant time savings if implemented over a series of
stops along a route. Curb extensions are most effective when traffic volumes are relatively
low and there are at least two lanes in the direction of travel to allow general traffic to pass
the stopped bus.
Changes to stop placement include the consolidation of stops along a corridor and stop
relocation. Stop consolidation reduces the number of stops the bus needs to make and may
allow for an increase in speed between them. Stop relocation from one side of an intersection
to the other can allow other priority treatments to be applied or improve the performance of
another priority treatment. Other measures include the enforcement of off-board payment
to reduce dwell times and improve overall bus running times.
1.5
Thesis Organization
The remainder of this thesis is organized as follows:
Chapter 2 reviews previous studies on identifying hotspots for transit and summarizes the
existing literature on the factors affecting bus running times and measures developed to
describe bus performance. It also summarizes previous research on the effects of bus priority
and traffic on performance.
Chapter 3 provides background on TfL’s bus network and the London Buses operating
environment, and describes TfL’s existing methodology for identifying bus hot spots. It
also includes a description of the various data sources used in this research.
19
In Chapter 4, the spatial boundary of a hot spot is considered to be the intersection level.
The chapter elaborates on three measures used to capture a route’s performance through an
intersection—running time variability, delay, and speed—and introduces intersection-level
performance measures.
Chapter 5 presents a methodology for identifying hot spots that uses the intersection performance measures developed in Chapter 4, as well as the the application of the methodology
on the London case study.
Chapter 6 discusses the factors that affect route performance on the London bus network.
This chapter also presents several regression models that explain route median speed and
percent of lost mileage due to traffic.
Chapter 7 summarizes the main findings from this work and suggests ideas for future research.
20
Chapter 2
Literature Review
This chapter summarizes previous studies that have focused on developing frameworks for
identifying potential bus priority locations. A discussion of the major research findings
on the various factors affecting bus running times is provided. This includes a more detailed review of research focusing on bus priority and traffic. Finally, a synthesis on work
contributing to the development of performance metrics for transit is presented.
2.1
Methodologies for Identifying and Evaluating Potential
Bus Priority Locations
2.1.1
Hot Spot Identification
A number of transit agencies have conducted studies on selecting corridors and/or intersections for transit signal priority (TSP) implementation. Smith et al. (2005) documented
eight case studies on the experience of North American public transit agencies. In general,
corridors were first chosen as part of transit-oriented strategic plans or based on transit ridership levels, service frequency and/or traffic conditions. Next, either all the intersections
along the corridor were chosen for TSP implementation or simulation was used to estimate
the benefits from the implementation of TSP on specific intersections.
Bernknopf et al. (2014) developed a method for evaluating and comparing one-mile road
segments in Philadelphia based on a set of criteria geared towards measuring the likelihood
21
of successful and cost-effective TSP implementation. Ten criteria were divided into four
categories: traffic, transit supply, transit demand, and planning priorities. Each category
was given a certain weight out of ten, distributed among the criteria within a category. Criteria related to traffic included segment and cross-street volume-to-capacity ratios, segment
and cross-street traffic volumes and signal density. Transit supply criteria measured segment and cross-street transit vehicle volumes, percentage of far-side stops, and total route
miles on a segment as a percentage of the total combined lengths of the route(s) operating
on that segment. The transit demand category consisted of three measures: segment and
cross-street passenger volumes, and the average passenger trip length. Finally the planning
priorities category included measures that indicated whether or not segments had been
identified as requiring TSP implementation in previous priority studies. An application of
this method on the Philadelphia network revealed that most of the highest-scoring segments
are major arterials and that these segments are not concentrated in a single location, but
rather distributed throughout the city.
A study which relied mainly on Automatic Vehicle Location (AVL) data to identify bus
priority hot spots in the Washington, D.C. area was conducted by Parsons Brinckeroff
(2012) for the Washington Metropolitan Area Transit Authority (WMATA). In this study,
roadway segments were assigned a score based on the regional average bus speed of 15 miles
per hour minus the actual bus speed multiplied by the number of buses in the time period of
study. Here, the actual speed was calculated as an average of WMATA and local agency bus
speeds, weighted by bus frequency. Each direction was treated separately and the direction
with the lowest speed was used for scoring. In cases where no actual speed data existed,
the scheduled speed estimated from the scheduled time and distance between time points
was used. The process to identify and prioritize hot spots began by selecting the top 200
scoring roadway segments. The 15 highest-scoring segments were reviewed to see if they
were surrounded by other segments in the top 200 to create larger hot spot corridors. A
number of secondary evaluation factors were considered in the post-processing phase, such
as the frequency of commuter routes, route level ridership, and reports from local operators.
The results of this study indicated that many of the slowest corridors overlap with locations
carrying the highest bus volumes, and most of the identified hot spot locations are located
in the Central Business District or other regional centers where transit ridership and traffic
congestion are high.
22
2.1.2
Evaluation Methodologies
Several studies have focused exclusively on evaluating the effects of TSP. Dion et al. (2006)
presented a methodology for evaluating the potential impacts of active TSP. Their two-step
approach first evaluated impacts at the intersection level, then used these impacts to develop
a corridor-level assessment. The model for intersection-level evaluation defined an intersection score as the product of a base score of 100 and a series of adjustment factors. The
adjustment factors are applied to account for various intersection parameters that may hinder the successful implementation of TSP. Examples of these parameters are the frequency
of priority requests, the proportion of green time available for allocation, the congested level
on prioritized movements, and other parameters related to roadway geometry, traffic flow
conditions, traffic signal operations and transit service elements. Adjustment factors were
determined from information found in the literature, theoretical analyses or simulation, and
are a function of one or more intersection parameters. An intersection score greater than
100 indicates a high potential for successful TSP deployment. The corridor-level evaluation
consisted of averaging the scores for each intersection in the corridor. The methodology
was assessed by comparing it to results of a simulation model; the results indicated that
intersection scores are generally consistent with the simulation results. Scores above 100
were obtained for intersections where bus priority provides a reduction in bus delays without
significant impacts on traffic.
Shourijeh et al. (2013) proposed a simulation-based framework which first tests and evaluates various TSP scenarios at the intersection level, then uses the evaluation results in
a mathematical optimization program to select the intersections and the movements (expressed in terms of scenarios) that will receive priority. Intersection-level scenarios are
evaluated according to two measures of marginal impact: the change in the average bus
delay from the baseline network (where no TSP is in operation) for all buses in the network, and the change in the average delay from the baseline network for all non-transit
vehicles through the intersection of interest. The objective function of the mathematical
model aims at identifying the intersection-scenario pairs that maximize the marginal delay
improvements. The framework was tested using the network of downtown Dover, Delaware,
and it was found an 18% decrease in the network average bus delay can be achieved with
the proposed TSP implementation pattern.
23
2.2
Factors Affecting Bus Performance
Many researchers have identified different factors affecting bus performance, and developed
methods to quantify them. Abkowitz and Engelstein (1983) developed empirical models
of mean running time and running time deviation estimated from data collected on bus
routes in Cincinnati, Ohio. They found that mean running time was strongly influenced
by trip length, the number of boardings and alightings, and signalized intersections. The
percentage of parking permitted along a link, the time of day and direction were found to
have small, statistically significant effects on running times. The running time deviation
model indicated that running time deviation propagates as vehicles move further from the
route origin, and is largely influenced by trip length.
Strathman and Hopper (1993) developed and estimated a multinomial logit model relating
bus transit on-time performance to a number of contributing causes. They found that
the probability of on-time arrival at a point along a route was negatively affected by the
number of alighting passengers, as buses progress further along the route, for services with
longer scheduled headways, and for PM peak outbound trips. They also found that driver
experience is important, with part-time drivers experiencing late arrivals significantly more
often than regular drivers.
In developing a framework to evaluate bus service reliability using data collected from ADC
systems and recommend strategies to improve service, Cham (2006) identified the most
significant causes of unreliability: deviations at terminals, passenger loads, running times,
environmental factors (such as traffic), and operator behavior. She applied the framework
on the Silver Line in Boston, and found that the variability of running times and headway
distributions were high, due mainly to deviations at the terminal, which propagate and further exacerbate reliability issues along the route. Ehrlich (2010) developed linear regression
models to measure the effect on service reliability of factors such as operator attributes, route
characteristics, ridership level, the availability of an AVL system, the contract structure,
road works, and seasonality. He used measures of service reliability to describe the passenger
experience, and found that service reliability decreased with increased precipitation, higher
ridership, and the installation of an AVL system. Another model estimation indicated that
seasonality and operator behavior have significant effects on service reliability, and that as
24
route length in the Central Business District increases, reliability decreases.
El-Geneidy et al. (2011) focused on reliability in bus operations, at both the route level
and the time point level. At the time point level, they estimated models to predict run
time, run time deviation, headway deviation and the coefficient of variation of run times
using automatically collected data from a particular route in the Metro Transit System,
Minnesota. They considered variables relating to time period, number of stops, boardings
and alightings, driver experience and delay at the first stop. They found that delay at the
first stop significantly increases the run time, and the experience of drivers affects run time,
headway deviation, and run time deviation. They also found that each scheduled stop adds
0.9% to the schedule deviation and recommended stop consolidation as a way to reduce
variability of service.
Several studies have analyzed the effect of real-time control strategies on bus performance.
Abkowitz and Lepofsky (1990) investigated the implementation of real-time headway based
control strategies on two high-frequency bus routes in Boston. Their results indicated that
there was a measurable improvement in running time variation that propagated downstream
across the entire route. In addition, the mean running time of the segment following the
control point decreased, as did the expected waiting time at the observation point immediately downstream of the control point. Furthermore, they showed that these improvements
in reliability could translate to savings in capital and operating expenses. Pangilinan et al.
(2008) evaluated the effectiveness of using real-time AVL to implement control strategies
and improve service reliability for a bus route in Chicago. They found that controlling the
departure headway and holding at key timepoints reduced the headway variation at each
timepoint compared to the baseline period.
2.2.1
Effect of Priority Measures on Bus Performance
In a study to determine the effectiveness of conditional TSP, Kimpel et al. (2005) measured
changes in bus running times, on-time performance, and excess passenger wait times following its implementation on several corridors in Portland. They concluded that the expected
benefits of TSP are not consistent across time periods, routes or performance measures, and
recommended an ongoing monitoring and adjustment plan to reap the maximum benefits
25
from TSP.
Schramm et al. (2010) performed a comprehensive analysis of 19 Bus Rapid Transit (BRT)
systems in the U.S. in an effort to determine the features that have the greatest effect in
reducing variability in travel time. They selected seven bus priority features and conducted
tests for each to determine their significance. These features include the running way, passing capability, station spacing, use of TSP, frequency of buses, use of level boarding, and the
fare collection process. They found that features that deal with general traffic—dedicated
running way, passing capabilities and running way with TSP—were the most effective. Similarly, Diab and El-Geneidy (2013) analyzed the impacts of different improvement strategies
on the service reliability of two routes in Montreal, using AVL and APC data. The strategies include the deployment of smart card fare collection system, operation of reserved bus
lanes, introduction of limited-stop bus services, use of articulated buses, and operation of
TSP. These strategies were implemented at different times over three years. They found
that the introduction of a smart card fare collection system increased bus running time and
running time variation. Exclusive bus lanes, articulated buses and limited stop service indicated mixed results in terms of improvements in running times, running time variation or
deviation from schedule. Buses equipped with TSP experienced a slight decrease in running
times, but no significant impact on variation or deviation from schedule.
Vedagiri et al. (2012) used microscopic simulation to study the effect of three bus priority measures on delay for both buses and general traffic through an intersection. They
conducted the analysis for different traffic volumes, and found that in general, there is a
decrease in delay for buses over the whole range of traffic flows, with higher reductions for
higher traffic volumes.
In general, previous research in this area indicates that the direct impacts of bus priority
on bus performance are oftentimes difficult to measure due to the complicated interactions
between buses, general traffic, passengers and other internal and external factors. The aim
of this thesis is to identify locations that may potentially benefit from the implementation
of priority measures following a more detailed examination of existing conditions, but not
recommend specific priority treatments.
26
2.2.2
Impact of Traffic on Bus Performance
Previous research aimed at quantifying the relationship between bus and car travel times
have found that car speeds were generally 40% to 60% times higher than bus speeds (Levinson, 1983) and that traffic congestion contributes a relatively small amount to low speeds
when compared to waiting at traffic signals, waiting for other buses to clear stops, and
serving passengers (Levinson et al., 1986). McKnight et al. (2003) conducted a study to
quantify the effect of traffic congestion on bus operations and cost. They estimated a model
of bus travel time rate as a function of car travel time rate, passenger boardings, and bus
stops, using data collected from two local bus routes in New Jersey. They found that, all
else equal, an increase of one min/mile of travel time for cars results in an increase of 0.73
min/mile of travel time for buses. However, buses were generally moving at about 60% the
speed of cars, a finding consistent with previous work (Levinson, 1983).
Researchers have also been interested in using bus travel time data to predict car travel
times on urban corridors (Bertini and Tantiyanugulchai, 2004; Chakroborty and Kikuchi,
2004). While not directly related to the objectives of this thesis, these studies still shed
light on the impact of traffic on bus performance.
2.3
Performance Metrics
Performance measures are an important part of an agency’s evaluation and monitoring
plans. Benn (1995) provides a synthesis of the current practice among North American
agencies for evaluating bus routes. Evaluation criteria include standards for route design,
schedule design, economic and productivity standards, service and delivery, and passenger
comfort and safety. Benn identified two standards for service delivery: on-time performance
and headway adherence. On-time performance deals with the deviation of service from the
schedule, while headway adherence is used the characterize evenness of service.
Sterman and Schofer (1976) were among the first researchers to study reliability of bus
services. They defined reliability as the inverse of the standard deviation of point-to-point
travel times.
In the Transit Capacity and Quality of Service Manual (TCQSM), Kittelson & Associates
27
et al. (2013) describe aspects of service quality that are important to passengers and readily
quantified. For fixed-route services, they divided quality of service measures into two categories: a) availability, and b) comfort and convenience. These measures are evaluated along
three dimensions: at transit stops, along route segments and corridors, and throughout a
system. They identified factors such as passenger load and reliability as important measures
of service quality, and used a rating scale from A to F (best to worst) to describe Level
of Service. Saberi et al. (2013) performed an analysis of the existing reliability measures
proposed by the TCQSM and developed new measures at the stop level to help agencies
improve schedules and operation strategies. These measures relied on the distribution of
headway deviations and for low-frequency service, the distribution of delay. The earliness
index is defined as the percentile rank of delay or headway deviation of zero. The width
index captures the width of the distribution of headway deviations in frequent services, and
is defined as the difference between an upper percentile and a lower percentile of headway
deviations, divided by the average scheduled headway.
Advances in ADC systems have facilitated the collection of a large number of disaggregate
observations on bus performance and allowed researchers to gain insight on the extreme
conditions of service, rather than just the mean or median conditions. Uniman et al.
(2010) used automated fare card data to measure the service reliability of rail systems as
experienced by passengers. They defined reliability buffer time as the difference between
an upper percentile value and the median of the journey time distribution. This metric
captures the amount of extra time that passengers need to account for, above the typical
travel time, to complete their journey on time. Ehrlich (2010) demonstrated how AVL
data may be used to improve service reliability and operations planning on the London bus
network. He introduced three measures of reliability which describe the entire bus passenger
experience: journey time, excess journey time, and reliability buffer time.
Trompet et al. (2011) assessed the strengths and weaknesses of four key performance indicators of service regularity used by urban bus operators: excess wait time, the standard
deviation of the difference between scheduled and actual headway, and the percentage of
headways that deviate by a specified amount from the scheduled headway, where the specified amount can be a relative value or an absolute number of minutes. They concluded that
excess wait time is the most effective for capturing the passenger experience.
28
Although measuring service reliability from the passenger perspective is important, the
focus of this thesis is to identify locations for potential bus priority implementation at
which operational reliability deteriorates. Sánchez-Martı́nez (2012) looked at metrics to
capture variability of bus operations as a tool to inform resource allocation decisions. He
defined several metrics of percentile-based spreads to measure variability of running times
across periods during which the operating environment may change. This thesis builds upon
one of these measures, the normalized mean spread, and will be discussed in more detail in
Section 4.2.1.
29
Chapter 3
Background on the London Buses
Case Study
The methodology for identifying potential locations for bus priority is applied to the London
bus network. This chapter first presents background information on the case study, which
includes a description of bus priority measures in London and a discussion on the current
process that Transport for London (TfL) uses for identifying hot spots. A description of
the data sources used for the analysis is also provided.
3.1
TfL and London Buses
TfL is the government body responsible for the public transportation system in greater
London. Its role is to implement the Mayor’s Transport Strategy and manage transportation services in London. This includes bus services (London Buses), heavy rail (London
Underground), light rail (the Docklands Light Railway and the London Tramlink), commuter services (the National Rail network and the London Overground) and ferry services
(London River Services). It also carries out road and traffic management duties and runs
Barclays Cycle Hire, London’s bike-sharing system (Transport for London, 2012c).
The business model for bus transit in London is a complex one. London Buses, a subsidiary
company of Surface Transport within TfL, is in charge of planning routes, specifying service
levels, and monitoring service quality. Services are operated by private companies. These
30
companies are responsible for generating schedules based on specifications set by London
Buses, managing their drivers, buses, and garage infrastructure, and are accountable to
TfL for their performance according to the terms of their contract. One indicator that
London Buses uses to monitor performance and calculate payments to operators is the
percentage of revenue vehicle miles in schedule that are operated, or total mileage operated.
Reasons for mileage not operated, or lost mileage, must be categorized by the operators
into different categories relating to operational conditions (such as traffic congestion or
diversions), failures (such as mechanical, driver or other reasons that may have prevented
the vehicle from completing its trip), or system recording failure. TfL also divides these
reasons into either deductible or non-deductible, depending on whether it deems the cause
out of the operator’s control. For example, lost mileage due to traffic is classified as nondeductible.
TfL also has road and traffic management responsibilities, carried out by London Streets, a
division of Surface Transport. London Streets is in charge of the management and operations
of the Transport for London Road Network (TLRN), which consists of 580 km of major
roads. TLRN makes up about 5% of London’s roads, but they carry more than 30% of its
traffic. London Streets also has a strategic responsibility of an additional 500 km of borough
roads, which together with the TLRN comprises the Strategic Road Network (SRN). Figure
3-1 is a map of the strategic roads in London. In addition, London Streets maintains all of
London’s traffic signals and manages the Congestion Charging Zone scheme (Transport for
London, 2012b).
The Mayor’s Transport Strategy sets out many of the policies that shape London’s transportation and road infrastructure. Achieving these policies means that London’s transport
system must offer increased capacity and connectivity, become more integrated and safe,
and support London’s growth and economic development. In addition, it should encourage
an increase in cycling and a mode shift to walking, public transport and ferry services.
In general, bus ridership in London continues to grow. In 2012, there were 56.8% more
unlinked trips by bus than in 2000; this increased demand reflects both increased population
in London and the expansion of the public transport network (Transport for London, 2013c).
Meanwhile, traffic volumes in the past decade have been steadily falling—by almost 21%
in central London. This decline is partly due to changes in the road network. Figure 3-2
31
Figure 3-1: Map of strategic roads in London (from Transport for London (2013a))
shows how London’s effective highway capacity for private vehicles has changed in the past
two decades. Central London has seen a reduction of about 30% of effective capacity since
1996 (Transport for London, 2011a).
The Mayor’s vision for cycling sets out a goal of delivering a 400% increase from 2001 in
the number of cycling trips and a 5% mode share for cycling by 2026. London has seen
a dramatic increase in the share of cycling and changes in the cycling network since 2000.
Cycling on London’s roads has increased by 173% since 2001. Large scale initiatives to
promote cycling include the construction of bike parking facilities with about 66,000 spaces
at railway and tube stations, the integration of a new bike-sharing system starting with
32
Figure 3-2: London’s highway capacity for private motorized vehicles (from Transport for London
(2011a))
around 6,000 bikes, and plans to construct twelve Cycle Superhighways, shown in Figure
3-3 (Transport for London, 2013b).
Increasing demand for bus services coupled with a shift of road capacity to accommodate
more sustainable modes of transit is an appropriate context for this research; identifying
locations where bus priority will yield the most benefits ensures that road space is used
effectively.
3.1.1
Bus Priority in London
There are a number of bus priority measures in London. Table 3.1 shows the types of bus
lanes available on the London road network, as of March 2013. Most bus lanes can be
used by taxis, motorcycles, and cyclists; they can be used by any vehicle during specified
periods of the day (usually evening off-peak hours). Bus gates are used to restrict access to
a particular street to buses only (or any other permitted vehicle). The gates can be traffic
signals, actuated by buses, or traffic signs.
33
34
Figure 3-3: London’s proposed cycle superhighways (from Transport for London (2013b))
Table 3.1: Breakdown of bus lane types in London
Number
Length (m)
Concurrent-flow
Bus lane
Gate
23
1
5,337
735
Contra-flow
Bus lane
Gate
989
91
267,807
9,891
Two-way
Bus lane
Gate
8
11
2,409
381
9
788
1,132
287,348
Bus only lane
Total
There are about 6,000 signalized intersections in London, of which around 4,100 are centrally
controlled by TfL’s real-time adaptive Urban Traffic Control system. The majority of these
intersections, about 3,200, are equipped with Split Cycle Offset Optimization Technique
(SCOOT), a traffic signal timing system that responds to traffic flow in real-time, models
the progression of traffic at the intersection, and adjusts signal timings accordingly. The rest
of the signals are Vehicle-Actuated (VA), or do not have a real-time modeling system. There
are about 800 SCOOT and 840 VA intersections equipped with bus priority (Transport for
London, 2012b).
The University of Southampton (2011) conducted an extensive study to evaluate the benefits
of signal bus priority in London. Sixty bus routes were chosen, representing a variety of
geographical locations, route attributes, frequency, and the type and number of signal
priority employed along the route, to use in a six-week pilot program. Signal priority at the
intersections through which these routes passed was turned off for three weeks, then on for
another three weeks in order to measure the impact of bus priority on end-to-end running
times and segment running times (from stop to stop).
Records of priority request acknowledgment indicated that around 10% of intersections
recorded less than 90% acknowledgment of priority requests, and around 5% recorded less
than 50% acknowledgment. Because signal priority was expected to be ineffective at these
intersections, delays were excluded from the analysis.
For end-to-end running times, it was determined that signal priority results in an average
overall savings in delay of 1.4 seconds/bus/intersections over the entire day (from 7:00
35
to 19:00), lower than the savings (up to 4 seconds/bus/intersection) recorded from earlier
surveys. In addition, several routes experienced an increase in delay after priority was turned
on. No significant correlation was found between the savings in delay and bus frequency or
the density of bus priority signals along a route.
3.1.2
TfL’s Methodology for Identifying Hot Spots
TfL’s current practice for identifying locations for potential bus priority treatments involves
the analysis of several datasets to determine areas where congestion is adversely impacting
bus performance.
An important input to the process is operator feedback. Information provided by bus
operators on delays is collected, and areas where there is a consistent reporting of congestion
are identified. Delays may be caused by road infrastructure or lack of enforcement (for
example, use of bus lanes by unauthorized vehicles). Lost mileage due to traffic is another
dataset considered; the forty routes that have lost more than 6% of their mileage each
period due to traffic for 13 periods1 are identified. Traffic data is also analyzed to identify
links where general traffic speeds are low and intersections where traffic experiences high
levels of delay.
In addition, a number of contextual datasets are considered. These include locations of bus
lanes, locations of National Rail stations within 100 meters of routes with the most lost
mileage due to traffic, and areas where roadworks have caused serious disruptions. Another
dimension considered is the level of demand for bus services. The Bus Origin Destination
Survey is used to determine areas of high ridership.
The above information is then combined and further analyzed to provide a list of locations
where more than one dataset indicates a bus reliability issue. The selected locations are
further examined to eliminate those that may have been recently intervened on or require
a solution that is infeasible in the short-term (for example, too expensive to resolve or
on-going development in the area). Figure 3-4 shows the areas that TfL has identified as
potential bus priority locations according to its most recent analysis.
1
TfL defines thirteen 28-day periods per financial year for reporting purposes.
36
Figure 3-4: TfL potential bus priority locations
The process followed by TfL has several merits: it combines various datasets to provide
important contextual information, and it is an exercise that can be easily repeated to
update the list of locations. However, it suffers from a number of shortcomings. First,
several of the datasets it relies on are based on subjective information. Bus operator reports
on delays are not easily validated, for example, using performance metrics based on bus
running times. Lost mileage due to traffic is another input that is based on operator
reports. In addition, lost mileage due to traffic is measured on a route-level basis, so it
does not provide insight on which specific locations along a route contribute to vehicles not
being able to complete trips due to congestion. Second, this method doesn’t attempt to
categorize locations into groups with similar characteristics; this would be useful because
specific solutions could be proposed for certain groups. Organization of hot spots into
groups could also provide means of prioritizing one group over another for further study.
Third, it does not provide an understanding of the specific underlying causes of poor bus
performance at the selected locations. This thesis attempts to address some of these issues
37
by presenting a more robust methodology of identifying locations that can benefit from
appropriate bus priority strategies.
3.2
Available Data Sources
Data used in this research was obtained from a variety of sources for a three-week period,
September 19 to October 10, in 2012. Weekends are excluded.
3.2.1
iBus
iBus is the AVL system used by TfL. It is based on the global positioning system (GPS)
and other supporting technologies to track each vehicle’s location. As a vehicle serves (or
passes) a stop, the system attempts to record the timestamps at which the bus approached
the stop, opened its doors, closed its doors, and pulled away from the stop. If all four events
are recorded, iBus stores the time at which the bus opened its doors as the arrival time,
and the time it closed its doors as the departure time. If either of the door events is missing
(for example, the bus did not serve passengers at that stop), the approaching time to the
stop or departing time from the stop is used. If only one of the four timestamps is available,
the time of this event is used as both the arrival and departure times at the stop (Gordon,
2012). iBus data can be used to calculate running times between any two stops, and the
dwell time at a stop.
3.2.2
Traffic Data
Link journey time data was obtained using information collected by Trafficmaster, TfL’s
traffic data supplier. Using data generated from the movements of GPS-equipped vehicles,
Trafficmaster is able to estimate average journey times on links and map it to England’s road
network. Each record contains a reference to the link in the road network along which the
average journey time is being reported, the date and 15-minute time period, the number of
observations collected on the link for that day and time period on which the average journey
time is based on, and the average journey time. Using this data, journey times and speeds
on London’s road network can be calculated for any time period during the day.
38
3.2.3
Accident Data
The United Kingdom’s Department for Transport provides detailed information on road
accidents collected from police reports. These reports record details of the location, time,
severity, and individuals and vehicles involved in the collision, along with a preliminary
evaluation of the cause.
3.2.4
Spatial Data
In addition to iBus and traffic data, the research in this thesis requires spatial information.
A list of the intersections and stops that a route passes through was obtained from TfL,
which includes spatial coordinates. In addition, locations of bus lanes (as a Geographic
Information Systems (GIS) layer) were obtained.
39
Chapter 4
Measuring Intersection
Performance for Bus Services
Intersections are oftentimes considered a bottleneck in bus operations. In general, signal
timings are set to minimize delays for general traffic volumes. Depending on the signal
phasing, whether there is any transit signal priority, and the level of congestion, buses may
wait longer than general traffic at intersections. In addition, with near-side stops, delays
for buses can increase even more as they serve passengers and wait to join the traffic stream
before crossing the intersection. A number of bus priority treatments at the intersection
level are available, as described in Section 1.4.2, to help mitigate these problems; thus,
measuring bus performance through an intersection is an important aspect for bus priority
studies. This chapter proposes a methodology to measure intersection performance from
the bus perspective. It also discusses the results from the application of the methodology
on a London Buses case study.
4.1
Methodology Overview
An important first step to characterize intersections is defining the extent to which bus
performance is measured. With Automatic Vehicle Location (AVL) data, it is possible to
obtain arrival and departure time at every stop along a route; thus, determining running
times across an intersection becomes a matter of selecting which stop before and which
40
stop after the intersection to use for analysis. Choosing the stops immediately before and
immediately after the intersection has a certain advantage; by taking the difference between
the arrival time at the stop directly after the intersection and the departure time directly
before the intersection allows the dwell time at either stop to be excluded from the running
time calculations.
An intersection is usually traversed by more than one route, with each route traversing
it in both of its directions or only one. In this research, only high-frequency routes are
included, because they provide enough running time data to obtain a representative sample
of performance. High-frequency routes also stand to gain the most from the implementation
of bus priority measures.
The measures used to describe bus performance through an intersection can vary depending
on the intended application. Studies focused on improving the passenger experience require
measures that describe the passenger’s journey and take into account level of demand. On
the other hand, studies whose primary goal is to improve operations employ measures that
describe route performance. Metrics are often defined to describe the performance of an
individual route at the segment, direction, or entire route level. At the intersection level,
measures need to take into account that several routes are passing through.
4.2
Metrics of Bus Performance through Intersections
Bus performance can be described by metrics that characterize different aspects of operations. In addition, bus performance may be affected by attributes specific to the intersection.
In this thesis, the selected metrics can be categorized into two groups: those describing running time variability, delay and speed of routes at the intersection level, and those related to
causes affecting performance. Using two or more of these metrics to highlight intersections
that consistently perform badly is a practical way to prioritize bus priority efforts.
4.2.1
Metrics at the Route Level
These metrics are calculated for individual routes through the intersection, and use the
running time distribution between the stops directly before and after the intersection. These
41
metrics form the basis of the intersection measures defined in Section 4.2.2.
Running Time Variability
There are several possible causes of variability of running times through an intersection.
This metric can be used to indicate issues related to:
• Signal timing. A bus may sometimes arrive at the intersection during a red phase,
and other times during the green phase. Depending on the signal timing settings, this
could induce a large amount of variability to the service.
• General traffic. Fluctuations in traffic volumes at an intersection negatively impact
bus running times. As buses approach the intersection, they may be held up by large
queues, or as they make left- or right-turning movements, the general traffic volumes
of conflicting movements may be significant.
• Pedestrians and cyclists. The presence of pedestrians and cyclists at an intersection
can have a significant effect on running time variability, depending on the volume and
the movement made by the bus through the intersection.
• Accidents and unplanned disruptions. These events are highly unpredictable, and any
occurrence can adversely affect running times.
• Bus driver behavior. Driving habits can vary greatly from one individual to another.
Some take on a more aggressive approach, for example, by accepting smaller gaps
when merging into general traffic or not making way for pedestrians, while others are
more yielding.
There are several measures that describe running time variability of a route, as mentioned
in Section 2.3. In this thesis, the metric developed by Sánchez-Martı́nez (2012) is used. He
defines measures that quantify the spread of the running time distribution and can be used to
characterize variability during heterogeneous time periods, or periods where the operating
environment may change resulting in different running time distributions. To overcome
both between-period and within-period variability, short, overlapping windows are defined,
in which running times are assumed to be homogeneous. A variability measure is calculated
for each of these windows, and then the arithmetic mean of the variability measures across
42
all windows is determined. For example, the variability is calculated for the AM peak, from
7:00 to 9:30, using windows of 30 minutes, shifted every 15 minutes. The first window is
centered at 7:00 and uses observations from 6:45 to 7:15. Then, the window is shifted 15
minutes until it is centered at 9:30. Equation (4.1) gives the mathematical formulation for
the running time variability V of a route for both directions (Sánchez-Martı́nez, 2012):
V =
1 X X
v(Td,w )
nw
(4.1)
w∈W d∈D
where
• D is the set of directions of a route,
• W is the set of a sequence of short, overlapping windows containing running time
observations,
• nW is the number of windows,
• Td,w is a set of running time observations in direction d, beginning at times contained
in window w, and
• v(Td,w ) is a measure of running time variability calculated using the set of observations
Td,w .
In the context of this research, the variability is measured for a route at the direction
level, rather than aggregating observations over both directions. The spread is used as
a measure of running time variability. Spread describes how dispersed the running time
distribution is by measuring the difference between an upper and lower percentile value. In
order to make consistent comparisons among routes traversing the same intersection, the
normalized spread is used. This is simply the spread divided by the median running time
of a route through the intersection. Using normalized spread as the variability measure
v(Td,w ) in Equation (4.1) provides a running time variability metric called the normalized
mean spread, and given by the following equation:
V =
1 X p90 (Td,w ) − p10 (Td,w )
nw
p50 (Td,w )
w∈W
43
(4.2)
where p90 (Td,w ), p50 (Td,w ), and p10 (Td,w ) are the 90th , 50th , and 10th percentile values of a
set of observations Td,w for direction d of a route in window w. A window size of 30 minutes
is used, shifted every 15 minutes. More details on the development of these measures can
be found in (Sánchez-Martı́nez, 2012).
Delay
Delay can be generally defined as an increase in travel times compared to travel times
during a free-flow period, usually off-peak hours. Delay can be attributed to issues related
to:
• General traffic. Roads are generally more congested during peak periods, as individuals need to travel to specific activities, such as work and school, during these times.
As a result, buses experience delays that they generally would not during periods of
low traffic volumes.
• Bus traffic. In a similar sense to general traffic, bus services are more frequent during
peak hours to accommodate increased demand, and buses may experience delays due
to increased bus traffic through an intersection.
• Signal phase settings. Certain movements may experience delays due to poor signal
phasing. During peak hours, signal timings may be set to accommodate movements
with heavy traffic, affecting buses performing other movements.
While many definitions of delay exist in the literature, this thesis uses a measure that
compares the free-flow speed of a route and its median speed as it crosses an intersection.
It is normalized by the free-flow speed. The delay D is given by the following equation:
D=
p85 (Sd,tf f ) − p50 (Sd,t )
p85 (Sd,tf f )
(4.3)
where
• d is the direction of the route,
• t and tf f are the time period of interest and the free-flow time period, respectively,
44
• p85 (Sd,tf f ) is the free-flow speed, determined by finding the 85th percentile of a set of
speed observations Sd,tf f for direction d during free-flow time period tf f , and
• p50 (Sd,t ) is the median speed of a set of speed observations Sd,t for direction d during
time period t.
For most routes, the free-flow time period tf f was defined as 22:00 to 5:00. In cases where
no trips where made during this times, the free-flow time period was defined from 5:00 to
6:00. There were only a number of routes that had no trips during either of these periods;
in this case, the route was assigned a free-flow speed that was determined by combining
the speed observations between 22:00 and 5:00 of all other routes that share the same stops
before and after the intersection, and finding the 85th percentile of the combined speed
distribution.
In traffic applications, the use of the 85th percentile of observed off-peak speeds to determine
free-flow speed is a common practice. In this research, using either the 85th or the 90th
percentile for free-flow speed was explored, and it was found that the former yielded more
realistic results.
Speed
Speed is an important metric to consider because it may shed light on the following specific
causes of poor bus performance:
• Intersection geometry. The number of approaches of an intersection or the number of
lanes in an approach determine intersection capacity, which has a significant effect on
bus speeds.
• Location of stops before/after the intersection. As buses leave or approach stops, they
need to accelerate and decelerate, respectively, so stop placement near an intersection
has an important impact on bus speeds.
The speed S of a route through an intersection is determined as the median of the set of
speed observations:
S = p50 (Sd,t )
45
(4.4)
where p50 (Sd,t ) is the median speed of a set of speed observations Sd,t for direction d and
time period t.
More explicitly,
S = p50 (
x
)
(Td,t )
(4.5)
where x is the distance traversed by the bus through an intersection, and Td,t is a set
of running time observations for direction d of the route and during time period t. The
distance x is the distance between the stops before and after the intersection.
4.2.2
Metrics at the Intersection Level
To describe intersection performance, the performance of the individual routes is combined
into a single metric. Two categories of intersection-level measures are developed in this
research. The first is an aggregate measure which captures the average overall performance
of an intersection. The second measure is a normalized range measure which characterizes
the amount of variation among routes in order to capture discrepancies in performance
among routes.
Aggregation
Any of the metrics defined in the previous section can be aggregated over all routes traveling
through the intersection using a weighted average by bus trips. This metric is appropriate
for determining if the intersection as a whole is a hot spot. For example, a higher value of
aggregate running time variability indicates that either all routes exhibit high variability in
running times or that only one or a few routes making a large number of trips through the
intersection experience high variability. The aggregation of a metric M for all routes can
be defined as:
P
MI =
r∈I
mr,t × nr,t
P
nr,t
r∈I
46
(4.6)
where
• r ∈ I is the set of routes crossing intersection I,
• mr,t is a measure describing bus performance for route r in time period t, and
• nr,t is the number of bus trips made by route r through the intersection during time
period t.
Range
To describe variation in individual route performance through an intersection, the range
between the maximum value and the minimum value among all routes using the intersection
for any of the metrics defined in the previous section can be used. A large range indicates
that a specific route experiences problems or that there is large variation in bus performance
among routes through the intersection. The range R is normalized by dividing it by the
maximum value in order to make consistent comparisons among intersections. Another
alternative would be to normalize the measure by dividing by the median value. It is given
by the following equation:
max{mr,t } − min{mr,t }
RI =
r∈I
r∈I
max{mr,t }
(4.7)
r∈I
where maxr∈I {mr,t } and minr∈I {mr,t } are the maximum and minimum values of metric m
in the set of all routes through the intersection.
4.2.3
Other Metrics
The following metrics are not necessarily used to classify intersections, but they are helpful
in providing insight on the potential causes affecting performance and may be used to
further examine a subset of identified intersections in more detail.
47
Bus Accidents
The number of bus accidents that occur within a certain radius of an intersection, when
used in conjunction with other metrics, can be useful in identifying intersections for indepth study. Intersections with a high number of accidents can be indicative of issues
with intersection geometry, lane markings, or enforcement, and resolving these issues can
help reduce delays for buses. The accident database, described in Section 3.2.3, was used
to determine the number of bus accidents per year that occur within 150 meters of an
intersection.
Total number of buses through intersection
Similar to accidents, heavy bus traffic can be a cause of poor intersection performance so
it would be beneficial to view this metric in relation to other metrics. However, it can also
be a consequence of poor conditions at the intersection. In addition, the total number of
buses per hour through an intersection can be used as a proxy for the number of passengers
traveling through the intersection and can be used for prioritizing certain intersections for
more detailed examination.
4.3
Data Needs and Processing
There are several datasets required to analyze the performance of buses through an intersection. This thesis uses three datasets provided by TfL:
• a list of transit node sequences,
• a list of bus stop sequences, and
• three weeks of iBus data for the period from September 19 to October 10, 2012.
The list of transit node sequences includes information on all the nodes that a route traverses
in both directions. A node can be either a junction node (an intersection) or an access node
(a stop) and is described by a transit node name, an alphanumerical reference number, and
a location in easting and northing. It also has a node sequence number to provide its order
48
in the nodes of a route and direction. Access nodes also contain an additional piece of
information called a stop ID.
The list of stop sequences provides information on all the stops of a route, including an
alphanumerical stop code and a stop sequence number, which is the position of the stop
along the route. Additionally, a stop has a stop ID, which is used to link these two datasets.
A list of transit node sequences containing important information on both the access (or
stop) and intersection nodes is then joined to iBus data. iBus data provides the arrival and
departure times at each of the stops along the route. For any intersection along a route,
the running time between stops can be determined.
4.3.1
Intersection Analysis Tool
Once the data is set up, the information can be organized into objects that are meaningful
for analysis. In this research, the following structure is adopted:
Intersection. An intersection is the basic entity used for analysis. It contains the following
information:
• A unique reference number
• A geographical location as described by easting and northing coordinates
• A list of routes that pass through the intersection, determined by identifying all
the routes which contain the intersection reference number in their list of transit
node sequences.
Route-direction. A route-direction through an intersection is uniquely identified by a
route name, a direction and a node sequence number. In addition, it contains the
following information:
• The stop directly before the intersection
• The stop directly after the intersection
• A list of running times that are computed by taking the difference between the
arrival time at the stop directly after the intersection and the departure time
directly before the intersection.
49
Figure 4-1: Goswell Road and Old Street intersection
• A label describing the stop before - stop after pair, assigned to all route-directions
sharing the same combination of stops before and after the intersection
As an example, Figure 4-1 shows the intersection of Goswell Road with Old Street. There
are five routes passing through the intersection. In some cases, two routes share the same
stops before and after the intersection, and therefore are assigned the same label, referred
to as an OD pair.
For each intersection, the running time data of all its route-directions is used to calculate
both individual route and intersection-level metrics for the time period of interest.
4.4
Intersection Characteristics in London
A total of 2,082 intersections were identified for analysis. The intersections cover a large
portion of the greater London area, reflecting the extensive bus service that TfL offers.
This section provides an overview of the performance of these intersections according to the
metrics defined in the previous section. The analysis focused on the AM period, defined
as 7:00 to 9:30. Figure 4-2 shows the correlation plots of the eight intersection measures
defined in Section 4.2. These plots are useful for determining general trends among the
50
Figure 4-2: Scatterplot of intersection measures
variables. The figure indicates that the aggregate and normalized range measures are poorly
correlated.
Table 4.1 shows the correlation matrix of the eight measures. The correlation coefficient ρ,
defined by Equation (4.8), measures the relationship between two variables.
ρX,Y =
cov(X, Y )
σX σY
(4.8)
where cov(X, Y ) is the covariance of two variables X and Y , and σX and σY are the standard
deviations of X and Y .
51
52
Aggregate RTV
Norm. RTV Range
Aggregate Delay
Norm. Delay Range
Aggregate Speed
Norm. Speed Range
Buses
Accidents
1.000
0.257
0.755
0.079
-0.406
0.319
0.181
0.127
Aggregate
RTV
1.000
0.141
0.601
-0.128
0.573
0.406
0.211
Norm. RTV
Range
1.000
-0.043
-0.439
0.281
0.124
0.037
Aggregate
Delay
1.000
-0.121
0.616
0.451
0.244
Norm. Delay
Range
1.000
-0.238
-0.337
-0.248
Aggregate
Speed
Table 4.1: Correlation matrix of measures
1.000
0.466
0.258
Norm. Speed
Range
1.000
0.526
Buses
1.000
Accidents
The correlation coefficient can vary in absolute value between 0 and 1; the stronger the
linear relationship between two variables, the closer the coefficient to 1. The sign of the coefficient indicates the direction of the linear relationship. Positive correlation means that the
variables move in the same direction; as one variable increases, the other increases, and vice
versa. Negative correlation indicates that variables move in the opposite direction.
The correlation matrix for the aggregate intersection measures shows that intersection variability and delay are positively correlated, with a moderate linear relationship. As expected,
intersection variability and delay are both negatively correlated with intersection speed, with
a somewhat weak linear relationship.
All three normalized range measures are positively correlated with each other, and exhibit
a moderate linear relationship. It is expected that as one of the normalized range measures
increases, the others also increase since a large variation in variability among routes also
translates into a large variation in delay and speed. Strong correlation between two variables
indicates that it may be enough to use only one of them in the characterization of hot
spots.
4.4.1
Aggregate Running Time Variability
Figure 4-3 shows the distribution of the aggregate running time variability, calculated using
the aggregate measure of Equation (4.6) where m is the metric for running time variability
as defined by Equation (4.2). The bars indicate the number of intersections that have an
aggregate running time variability in the interval indicated on the x-axis. As can be seen
from the figure, the majority of intersections fall within the range of 0.5 to 0.9. A variability
of around 1 indicates that the spread of running times is almost equal to the median running
time. However, a small number of intersections have an aggregate running time variability
greater than 1.5.
The vertical lines on the figure show the 20th , 40th , 60th , and 80th quintiles, when ordered
by increasing aggregate running time variability and the values mark the limits between
consecutive groups. This shows, for example, that the twenty percent of intersections with
the highest running time variability have values greater than 0.85.
Figure 4-6 shows the locations of intersections color-colored according to the quintile they
53
0.512 0.632 0.740 0.851 1 400 0.9 350 0.8 0.7 300 0.6 250 0.5 200 0.4 150 0.3 >3 2.8 -­‐ 2.9 2.6 -­‐ 2.7 2.4 -­‐ 2.5 2.2 -­‐ 2.3 2 -­‐ 2.1 1.8 -­‐ 1.9 1.6 -­‐ 1.7 1.4 -­‐ 1.5 1.2 -­‐ 1.3 1 -­‐ 1.1 0 0.8 -­‐ 0.9 0.1 0.6 -­‐ 0.7 50 0.4 -­‐ 0.5 0.2 0.2 -­‐ 0.3 100 0 -­‐ 0.1 Number of Intersec/ons 450 0 Aggregate Running Time Variability Figure 4-3: Distribution of aggregate running time variability
belong to from green for the first quintile to red for the fifth quintile. Q1 represents the
lowest fifth of intersections, Q2 the second fifth, and so on. Intersections belonging to the
fourth and fifth quintiles are generally located in central London or along major roads in
the network.
4.4.2
Aggregate Delay
The distribution of the aggregate delay, based on the aggregate measure of Equation (4.6),
where m is the metric for delay defined by Equation (4.3), is shown in Figure 4-4. The lowest
fifth of intersections have an aggregate delay between 0.10 and 0.34, while the highest
fifth have values between 0.52 and 0.85. Similar to aggregate running time variability,
intersections in the top 20th percentile are located in central London or along major roads,
as shown in Figure 4-7.
54
0 55
Aggregate Speed (mph) Figure 4-5: Distribution of aggregate speed
200 150 100 50 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0.95 -­‐ 1 250 > 40 300 0.9 -­‐ 0.95 350 38 -­‐ 40 400 0.85 -­‐ 0.9 450 36 -­‐ 38 Figure 4-4: Distribution of aggregate delay
0.8 -­‐ 0.85 13.86 11.38 9.59 7.67 Agreggate Delay 34 -­‐ 36 32 -­‐ 34 30 -­‐ 32 28 -­‐ 30 26 -­‐ 28 24 -­‐ 26 22 -­‐ 24 20 -­‐ 22 18 -­‐ 20 16 -­‐ 18 14 -­‐ 16 12 -­‐ 14 10 -­‐ 12 8 -­‐ 10 500 6 -­‐ 8 4 -­‐ 6 2 -­‐ 4 0 -­‐ 0.05 0 0 -­‐ 2 Number of Intersec/ons Number of Intersec/ons 0.521 0.457 0.401 0.343 400 350 300 250 200 150 100 50 4.4.3
Aggregate Speed
Figure 4-5 shows the distribution of aggregate speed. Most intersections have an aggregate
speed between 8 and 12 mph, which is reasonable for urban areas. Intersections with higher
speeds are located in the outer areas of London, as seen in Figure 4-8, while intersections in
central London experience the slowest speeds. This confirms the recent trend that buses in
central London are experiencing lower speeds due to improvements aimed towards cyclists
and pedestrians.
4.4.4
Normalized Running Time Variability Range
Figure 4-9 presents the distribution of the normalized range of running time variability,
determined using the range measure of Equation (4.7), with running time variability metric
m defined by Equation (4.2). Since this measure is normalized using the maximum value of
running time variability through an intersection and the range is a positive value, it can only
take on values between 0 and 1. It is important to note here that values of 0 correspond to
intersections where there is only one route, a total of 76 intersections. A normalized range
of 0.5 means that the route with the lowest value has a running time variability half of
that of the route with the highest value. Figure 4-12 shows that intersections with higher
normalized ranges of running time variability are found throughout the greater London
area.
4.4.5
Normalized Delay Range
The distribution of the normalized range of delay is shown in Figure 4-10. The interpretation for this metric is similar to that of normalized range of running time variability —
intersections with larger values indicate that a certain route is experiencing higher delays
compared to the rest of the routes passing through the intersection. Figure 4-13 shows the
spatial distribution of the normalized range of delay.
56
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Figure 4-6: Quintile map of aggregate running time variability
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Figure 4-7: Quintile map of aggregate delay
Delay
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Figure 4-8: Quintile map of aggregate speed
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0 60
Normalized Delay Range Figure 4-10: Distribution of normalized delay range
150 100 50 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0.95 -­‐ 1 200 0.95 -­‐ 1 Figure 4-9: Distribution of normalized running time variability range
0.9 -­‐ 0.95 0.507 0.398 0.299 Normalized Running Time Variability Range 0.9 -­‐ 0.95 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.163 250 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0 -­‐ 0.05 0 0 -­‐ 0.05 Number of Intersec/ons Number of Intersec/ons 0.527 0.415 0.308 0.184 250 200 150 100 50 0.584 0.436 0.304 0.155 180 160 Number of Intersec;ons 140 120 100 80 60 40 0.95 -­‐ 1 0.9 -­‐ 0.95 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0 0 -­‐ 0.05 20 Normalized Speed Range Figure 4-11: Distribution of normalized speed range
4.4.6
Normalized Speed Range
The range metric of Equation (4.7) can also be defined for median speed. Since speed is
always positive, the range of speed through the intersection is also always positive. Thus,
the normalized range can only take on values between 0 and 1. The distribution of the
normalized range of speed is shown in Figure 4-11. Eighty percent of the intersections have
a range between 0 and 0.58. Figure 4-14 shows that intersections that have large differences
in speed among their visits are distributed throughout London.
4.4.7
Bus Accidents
Figure 4-15 shows the location of accidents where buses were involved in 2012. Looking at
the distribution, it is clear that there are some corridors where there is a high number of accidents occurring. Data on the duration of these accidents was not available, so determining
their direct effect on bus performance was not possible.
61
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Delay
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Figure 4-14: Quintile map of normalized speed range
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Figure 4-15: Location of bus accidents in 2012
500 450 350 300 250 200 150 100 Total Number of Buses per Hour Figure 4-16: Distribution of total number of buses per hour
65
> 300 280 -­‐ 290 260 -­‐ 270 240 -­‐ 250 220 -­‐ 230 200 -­‐ 210 180 -­‐ 190 160 -­‐ 170 140 -­‐ 150 120 -­‐ 130 100 -­‐ 110 80 -­‐ 90 60 -­‐ 70 40 -­‐ 50 0 20 -­‐ 30 50 0 -­‐ 10 Number of Intersec;ons 400 66
Figure 4-17: Spatial distribution of total number of buses per hour
TLRN
SRN
Number of Buses per Hour
8.9 - 30.0
30.1 - 60.0
60.1 - 90.0
90.1 - 120.0
> 120
Legend
4.4.8
Total Number of Buses
Figures 4-16 and 4-17 show the distribution of the total number of buses passing through
the intersection per hour. Very few intersections have less than 30 buses per hour. In
some cases, an intersection may have 400 buses per hour passing through the intersection.
This corresponds to about 6 buses at the intersection every minute. These intersections are
usually large transfer points at rail stations, such as Oxford Circus.
4.5
Summary and Conclusions
This chapter presented a methodology to measure intersection performance from the bus
perspective. The running time distribution of a route through an intersection was used to
define three route-level measures: running time variability, delay, and speed. Using all routes
that pass through the intersection and only those that belong to high-frequency routes, two
intersection-level measures were defined that relied on route-level measures. The first is
an aggregate measure that weighted the performance of a route by the number of trips it
made through the intersection. The second is a range measure that quantified the difference
between the worst-performing and best-performing routes through the intersection. A total
of six measures was used to describe intersection performance: the aggregate running time
variability, the aggregate delay, the aggregate speed, the normalized range of running time
variability, the normalized range of delay, and the normalized range of speed. In addition,
the characteristics of two measures related to causes (and possibly even consequences), the
number of bus accidents per year and the total number of buses through an intersection,
were also described.
Each of these aggregate and normalized range measures was used to describe London’s intersections by examining its distribution and producing a map that indicated which quintile
an intersection belonged to using a color range from green to red. In general, the measures
indicated that most intersections fall around the average value, with only a few intersections exhibiting extreme performance. The aggregate measures showed that intersections
belonging to the fifth quintile are located in central London or along major roads. The
range measures indicated that intersections having large differences among their routes can
be found throughout London.
67
Chapter 5
Identification of Hot Spots in
London
The previous chapter defined several metrics that describe the performance of buses through
an intersection. This chapter explores how these measures can be used to identify hot spots.
Section 5.1 discusses the methodology to identify hot spots at the intersection level, and
Section 5.2 presents the results from the application of the methodology to the London bus
network. An analysis of the hot spot locations identified by Transport for London (TfL) is
presented in Section 5.3. Finally, the chapter concludes with recommendations regarding
the identification of hot spots in the London bus network in Section 5.4.
5.1
Methodology Overview
The purpose of identifying intersection hot spots is to provide a transit agency with a subset
of intersections that can be examined in more detail for their contribution to performance
deterioration and potential for improvement. Identifying this subset of intersections depends on the characteristics used to group intersections and the definition of a hot spot.
For example, an agency may decide that intersections where buses experience large delays
warrant some bus priority measures. Here, the issue is determining what constitutes large
delays. Identifying several groups of intersections—intersections with high, medium, or low
delays—is helpful for prioritizing efforts but involves systematically defining the criteria
68
that an intersection must meet to belong in a group. Alternatively, agencies may be interested in categorizing intersections in a way that provides them with information on the
potential for implementing priority measures. Grouping intersections by their geometry
type or their volume-to-capacity ratio can distinguish between intersections where it may
be feasible to remove a lane from general traffic and add a queue jump lane for buses at the
intersection. However, these intersection characteristics are often difficult to obtain. On
the other hand, Automatic Vehicle Location (AVL) data provides a wealth of information
on bus operations that should be taken advantage of for the purpose of identifying locations
for bus priority. The measures defined in Chapter 4 are an example of how the data can be
used to characterize intersection performance. The problem at hand becomes a matter of
defining a consistent and logical procedure that uses these measures to produce a subset of
intersections which may benefit from bus priority implementation.
Two categories of measures that relate to performance were developed in Chapter 4: the
aggregate measure, and the normalized range measure. Another category of measures relating to the causes or consequences of poor performance was also developed: the number of
bus accidents that occur within the intersection in a year, and the total number of buses per
hour passing through the intersection. An interpretation of the aggregate and normalized
range measures in terms of the intersection problems they characterize and the possible
solutions for improving bus performance is warranted as an initial step.
The aggregate measure looks at an aspect of bus performance (the running time variability,
the delay, or the speed) across all routes traveling through the intersection and defines an
average measure of the overall performance of the intersection. An intersection with a higher
value of running time variability, for example, indicates that most of the routes exhibit high
variability in running times. The implication for an intersection through which all routes
experience a deterioration of performance is that the intersection will likely require extensive
structural or operational changes in order to improve its performance. These major changes
may include changes to the intersection geometry, the optimization of signal timings, or the
relocation or removal of bus stops near the intersection. When examining the possibility
of such alternatives, careful consideration must be given to their impacts on general traffic,
pedestrians, and cyclists, the amount of investment needed, and most importantly, the
expected change in the performance of buses.
69
On the other hand, the range measures describe the variation in the performance among
all routes passing through the intersection. It is important to capture this dimension of
intersection performance. For example, it may be the case that one route through the
intersection experiences a disproportionately larger variability than the rest of the routes.
This may not be captured by the aggregate measure because the route makes a lower number
of trips through the intersection so its higher variability compared to other routes is not
reflected. However, the normalized range may indicate that smaller scale interventions
to the intersection may significantly improve the performance of one of the routes passing
through it. Possible interventions may include changes to the signal phase timings to provide
priority to one of the movements through which the route passes, or if the impacts on other
movements is too severe, the rerouting of the route such that it makes another movement
through the intersection. In any case, any changes considered to the intersections should
be carefully evaluated in the context of the resulting changes on other routes, other users
of the intersection, and possible intersection constraints.
5.1.1
Methods Considered for Identification of Intersection Hot Spots
Based on the above discussion, it follows that it is important to capture two dimensions of
intersection performance: a) in terms of its overall performance, and b) with respect to any
discrepancies among the routes passing through. In addition, factors that are hypothesized
to contribute to poor performance may also be considered as an additional dimension for
grouping of intersections. There are a number of methods that were considered that use
the various measures in order to categorize intersections according to their performance
with the purpose of identifying those that exhibit the worst performance as intersection hot
spots. These methods are clustering, defining a composite score, and ranking.
Clustering
Unsupervised or clustering methods are algorithms that focus on the grouping of objects
without prior information on the characteristics required for an object to belong to a certain
group. Clustering algorithms aim at grouping objects such that objects in the same group
are more similar (according to some characteristics) to each other than those in other groups.
70
K-means is one of the most popular clustering algorithms where the number of clusters is
already specified. K-means assigns objects into the nearest cluster such that the squared
distances from the cluster center are minimized and the distances from other cluster centers
are maximized. (See (Jain and Dubes, 1988; Jain et al., 1999; Xu et al., 2005) for more
information on clustering algorithms.)
The variables used in clustering have important implications for the output of the algorithm. In the case where two or three variables are used in the clustering analysis, a visual
representation as a 2D or 3D graph can help discern the clusters by examining where the
objects are located according to these variables. Using the measures developed in Chapter
4, Figure 5-1a shows how the sample of intersections in London performs according to the
aggregate measures of running time variability, delay, and speed, while Figure 5-1b shows
the distribution of intersections according to the normalized range measures. It is clear that
no natural grouping of intersections arises according to a combination of these variables,
and thus, it makes it difficult to categorize intersections using clustering analysis.
In any case, the k-means clustering algorithm was applied to the sample of intersections with
different combinations of aggregate measures, normalized range measures, and measures
that relate the performance of intersections to causes. The results indicated that k-means
was not successful in defining distinct clusters, making it difficult to define a group of
intersections with specific characteristics that indicate bad performance.
Composite Score
This methodology provides a single score to each intersection based on the scores for each
individual aggregate and normalized range measure. The individual scores are normalized
to a 0 - 100 scale in order to address the challenge of combining different components that
each have different scales. The normalization is done by dividing an intersection’s measure
by the maximum measure considering all intersections. For example, the individual score
of the aggregate running time variability is given by Equation (5.1):
Score VI =
VI
× 100
maxI {VI }
71
(5.1)
80
70
60
50
Aggregate Delay
1.0
Normalized Delay Range
40
0.9
30
20
10
Aggregate Speed (mph)
0
0.1
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.2
0.3
0.4
0.5
0.6
0.7
0.8
4.0
Aggregate RTV
0.8
0.6
0.4
0.2
0.8
0.6
0.4
0.2
0.0
Normalized Speed Range
1.0
(a) Aggregate RTV, delay, and speed
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Normalized RTV Range
(b) Normalized range of RTV, delay and speed
Figure 5-1: Intersection performance measures
72
where VI is the aggregate variability for intersection I as defined by Equation (4.6) in
Section 4.2.2.
This normalization guarantees that intersections are given a score out of 100 and compared
to the worst performing intersection according to the corresponding measure. The composite
score CSI for intersection I, as defined by Equation (5.2), is simply the sum of its individual
scores S(I,M ) . Intersections are then ranked using the composite score.
CSI =
X
S(I,M )
(5.2)
allM
Other factors related to causes can be included in the scoring. This provides a means of
prioritizing certain intersections. For example, two intersections may perform relatively
the same according to the aggregate and normalized range measures, and thus have very
similar composite scores, but one intersection may have a higher number of buses traveling
through. Including this factor in the score will rank the intersection higher, and prioritize
it for bus priority efforts, thus providing benefits to a larger number vehicles.
However, using a composite score to describe an intersection’s performance makes it difficult
to discern which aspects of performance are contributing the most to the composite score.
It is important to distinguish between the different aspects because the strategies used
to address intersections with high variability, for example, are often different than those
needed to address intersections with high delays or low speeds. In addition, this method
does not categorize intersections into specific subsets, making it difficult to select the hot
spot intersections that would benefit from the implementation of bus priority.
Ranking
One of the simplest ways of selecting intersections for the implementation of bus priority
treatments is by ranking, or ordering of intersections based on a certain criterion or set of
criteria. Ranking by a single measure, for example the aggregate running time variability,
provides information on what are the implications of selecting the worst intersections in
terms of variability. Aggregate variability is indicative of problems that may be caused by
factors different than those causing higher aggregate delay. On the other hand, choosing to
73
select the worst intersections by ranking according to a normalized range measure means
that movement-specific issues will likely be tackled. Developing a method that looks at
the ranking of intersections according to more than one measure has certain advantages.
It ensures that the subset of intersections identified as hot spots by ranking, using more
than one measure are the worst in terms of all possible dimensions: overall performance and
movement-specific performance problems in terms of variability, delay and speed. This helps
prioritize efforts towards intersections that need it the most. The next section describes the
proposed ranking methodology in more detail.
5.1.2
Ranking Methodology
One possible way of ranking intersections by multiple measures is by selecting the worst
performing of intersections in each measure, identifying the distinct intersections, and determining according to how many measures each intersection appears in the worst subset.
To illustrate this, first, the top 40 intersections that appear in the following measures were
identified: the aggregate measures, the normalized range measures, and those related to
causes of poor performance (that is, the number of accidents, and the total number of buses
per hour), a total of 8 measures. In the 8 individual lists of top 40 intersections (one for each
measure), there were 249 distinct intersections. Next, the number of measures in which each
intersection was ranked in the top 40 was determined; the distribution is shown in Figure
5-2. The figure indicates that none of the intersections are the worst performing in all eight
measures. In fact, there were no intersections that ranked in the top 40 in more than 3
measures. The majority of intersections, about 77%, are the worst performing according
to only one measure. This suggests that not many intersections are worst performing according to multiple criteria, from overall performance to movement-specific issues, to safety
concerns.
Table 5.1 shows, for intersections ranked three times in the top 40 list, the measures according to which they were ranked. The table indicates that the most common combination
is intersections appearing in all three of the aggregate measures. This suggests that intersections that are worse performing in one of the aggregate measures are usually worse
performing in the other measures. In addition, intersections who rank in the top 40 in the
normalized running time variability range also rank in the top 40 in the normalized range of
74
250 Number of Intersec/ons 200 150 100 50 0 1 2 Number of Times in Top 40 3 Figure 5-2: Distribution of number of times an intersection appears in the top 40
speed. Because it is unlikely that intersections will be the worst performing across all eight
measures, it may be more informative to use subsets of these measures, especially since the
different performance groupings indicate different underlying causes and point to different
mitigation strategies.
An intersection that is top ranking in all three aggregate measures indicates that the buses
that pass through the intersection experience, as a whole, a deterioration in the variability, delay and speed, so it would be important to identify these intersections as hot spots.
Similarly, it would be important to identify intersections that are the top ranking in all
the normalized range measures and see whether the route or routes that vary greatly from
the rest of the routes passing through the intersection in terms of higher variability, higher
delay and lower speeds belong to the same movement. In addition, the weak or moderate
correlation between variability, delay, and speed, in terms of aggregate measures or normalized range measures, (as indicated by the correlation matrix in Table 4.1) suggests that
they each capture different aspects of performance. If two variables were strongly correlated, then it may be more reasonable to use only one. Because it is unlikely that a single
intersection will be top ranking in all six measures, finding those that are top ranking in
75
76
x
x
x
x
J2650
J2157
J2533
J7736
x
J2632
x
x
J1143
x
J1128
x
J1105
x
x
x
J5537
RangeDelay
J2113
x
x
RangeRTV
J3508
x
x
x
x
x
AggSpeed
x
x
x
x
x
x
AggDelay
J3654
J1773
J4739
AggRTV
Intersection
x
x
x
x
x
x
x
RangeSpeed
x
x
x
x
x
NumBuses
Table 5.1: Distribution of measures for intersections appearing in three top 40 lists
x
x
x
x
x
Accidents
the aggregate measures first, then in the normalized measuring will identify two sets of
intersections, those that have overall performance issues and those with movement-specific
issues.
Therefore, the following approach is adopted. First, intersections are ranked according to
each aggregate measure separately. Intersections are ranked in decreasing order of variability
and delay, because higher values of these measures indicate worse performance. On the other
hand, intersections are ranked in increasing order of speed, because lower speeds mean worse
performance. Defining what proportion of intersections, such as the top 10% or 20%, to
select as the top ranking is important. Selecting a larger proportion of intersections will
result in the identification of a larger list of potential hot spots. Depending on the scope
and resources allocated to bus priority studies, it may be beneficial to start with a smaller
proportion of intersections then expand the set of hot spot intersections by looking at a
larger proportion. A similar procedure is then performed by ranking intersections in each
normalized range measure separately. In this case, intersections are ranked in decreasing
order because higher ranges indicate larger variation among routes. Then the top 10%, or
20% of intersections in each range measure are selected, and the intersections that are the
top ranking in all three are selected as hot spots.
Based on the above process, it may be that the intersection which is, for example, the
worst performing in a single measure is not identified as a hot spot because it is not worst
performing according to the other two measures. In this case, it is still necessary to identify
this intersection and include it in the list of hot spots because of the severe deterioration in
performance that buses experience at the intersection. The resulting set of hot spots includes
intersections that are top ranking in all three of the aggregate measures, all three of the
normalized range measures, and intersections that are the worst performing when ranked
by the individual measures, if they are not included already. Determining what constitutes
the top ranking intersections may not be straight-forward. These extreme cases may be
selected using judgement on what constitutes extreme poor performance for an intersection.
For example, an intersection where buses travel through at speeds lower than walking speed
can be considered to be an extreme case that needs to be addressed. Another option would
be to look at intersections that are outliers, or perform significantly different than the rest
of the intersections. Finding these outliers or looking for natural breaks between groups of
77
intersections may be difficult if the distribution of intersection performance does not indicate
a group that varies greatly from the rest of the intersections. Another option would be to
select the top absolute number or percentage of intersections from the entire sample.
After obtaining a list of intersection hot spots, the question of which intersections to target
arises. The number of buses can be a criterion used to prioritize intersections for further
examination. The total number of bus trips per hour can be considered as a proxy for
ridership demand, so intersections with a higher number of buses may have higher priority
for priority implementation because they serve a higher number of people who will ultimately
benefit from these priority measures. An alternative may be to examine intersections that
have the highest number of accidents first because improving safety for passengers is a
priority for a transit agency.
Figure 5-3 outlines the methodology described in this section.
5.2
Application of Ranking Methodology
Based on the methodology described in the previous section, the ranking methodology
was applied to 2,082 London intersections, using AVL data from a three-week period from
September 19 to October 10, 2012. The application focused on intersection performance
during the AM peak, from 7:00 to 9:30.
5.2.1
Combined Ranking
For each of the aggregate measures, the intersections were ranked appropriately and the
top 208 intersections were selected, which corresponds to about 10% of the sample. This
produced three lists of 208 intersections each, in which there were 431 distinct intersections.
Table 5.2 presents the breakdown of these intersections according to the number of times
they appear in the top-ranking lists. Sixty-six percent of the distinct intersections are
considered to be the worst performing according to only one measure, while 24% appear in
two of the top-ranking lists. Forty-five intersections in the London network appear to be
the worst-performing according to all three measures, and are identified as candidate hot
spots.
78
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Speed)
Range)
Delay)
Range)
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Identify)intersections)that)
are)top)ranking)in)ALL)
three)normalized)range)
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Individual)ranking)
NORMALIZED)RANGE)MEASURES)
Combined)ranking)
Figure 5-3: Hot spot identification methodology
Supplementary)set)of)hot)spot)
intersections)
Aggregate)
RTV)
Identify)top)ranked)intersection(s))in)
EACH)aggregate)measure)individually))
Final)set)of)hot)spot)
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intersections)
Identify)intersections)that)
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three)aggregate)measures)
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Individual)ranking)
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Table 5.2: Combined ranking using aggregate measures - top 10%
Number of Intersections
Percent of
Distinct Intersections (%)
Only 1 aggregate measure
283
65.7
RTV
Delay
Speed
93
71
119
21.6
16.5
27.7
Only 2 aggregate measures
103
23.9
RTV and Delay
RTV and Speed
Delay and Speed
59
11
33
13.7
2.6
7.7
In all 3 aggregate measures
45
10.4
Total
431
100
As was mentioned earlier, the selection of the top 10% intersections in each measure is
arbitrary. If the subset of top ranking intersections according to all three aggregate measures was expanded to 20%, the breakdown of intersections appearing in one, two or three
measures is shown in Table 5.3. The number of hot spot intersections increases from 45 to
126. The proportion of intersections that are top ranking in one, two or three measures out
of all distinct intersections stays relatively the same.
Selecting a higher threshold, such as 20%, allows the identification of a larger number of
intersections and the interpretation of the corresponding set is that it represents a candidate list of hot spots. The intersections in the list should be examined in greater detail.
Furthermore, the larger set allows for the grouping of spatially close intersections in order
to identify corridor hot spots.
After identifying these intersections, it is important to examine them in the context of their
location and other attributes that may explain their performance. Figure 5-4 shows the
location of the intersections distinguishing between those that belong to the top 10% in
all three measures, only two, or only one measure. The majority are on major roads and
a number of the intersections are in proximity to other hot spot intersections. This may
indicate that the poor performance of buses is not due to an isolated intersection, but rather
it is a manifestation of corridor-level issues. The intersections may belong to corridors with
high traffic volumes, or the green phasing along the corridor may be uncoordinated, and as
80
Table 5.3: Combined ranking using aggregate measures - top 20%
Number of Intersections
Percent of
Distinct Intersections (%)
Only 1 aggregate measure
388
51.4
RTV
Delay
Speed
126
93
169
16.7
12.3
22.4
Only 2 aggregate measures
241
31.9
RTV and Delay
RTV and Speed
Delay and Speed
120
44
77
15.9
5.8
10.2
In all 3 aggregate measures
126
16.7
Total
755
100
a result, buses experience low speeds and large delays throughout. In addition, the figure
shows the corridors, outlined in purple, that TfL has identified as bus hot spots. It is
interesting to see that many of the intersections identified through the combined ranking
of the aggregate measures are present in some TfL hot spots, while in other corridors, the
combined ranking did not identify any hot spot locations.
The combined ranking using the range measures is performed next. In the three lists of
208 (10% of the total) intersections that are top ranking in each normalized range measure,
there were 460 distinct intersections. However, only 23 intersections are top ranking in
all normalized measures. The breakdown of intersections appearing in one, two or three
measures is shown in Table 5.4, and their spatial distribution in Figure 5-5. The spatial
distribution indicates that intersections that are identified as hot spots by the normalized
range measures are not necessarily on major roads, but rather spread out across London.
The combined ranking using aggregate measures identified many intersections in central
London, where a high number of routes travel through intersections and congestion is likely
to be high. On the other hand, the normalized range measures identify intersections where
only a few routes experience a deterioration in service, and this occurs throughout the bus
network. Again, a number of these intersections are found within the corridors identified
by TfL.
There were 120 intersections identified by the combined ranking of the aggregate measures
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Legend
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S5
Number of Range Measures
!
1
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3
TfL Potential Bus Priority Locations
Figure 5-5: Combined ranking - normalized range measures
83
Table 5.4: Combined ranking using normalized range measures - top 10 %
Number of Intersections
Percent of
Distinct Intersections (%)
Only 1 norm. range measure
319
69.3
RTV
Delay
Speed
111
101
107
24.1
22.0
23.3
Only 2 norm. range measures
118
25.7
RTV and Delay
RTV and Speed
Delay and Speed
40
34
44
8.7
7.4
9.6
In all 3 norm. range measures
23
5.0
Total
460
100.0
also identified by the combined ranking of the normalized range measures, using the top
10% intersections. Table 5.5 shows, for the intersections in common, how many times they
appeared in the top 10% across all six measures. For example, there were no intersections
that were in the top 10% in all three aggregate measures and in the top 10% in all three
normalized range measures.
Table 5.5: Number of times common intersections appear in six measures - top 10%
Normalized Range
Aggregate
3 measures
2 measures
1 measure
3 measures
2 measures
1 measure
0
0
3
3
6
29
7
23
49
If the subset of top ranking intersections was expanded to 20%, 339 intersections are now
identified to be top ranking by both the combined ranking of the aggregate measures and the
combined ranking of the normalized range measures. Table 5.6 shows how many times the
intersections in common appear across all six measures. With a larger subset of intersections,
there are now six measures that are top ranking in all six measures.
84
Table 5.6: Number of times common intersections appear in six measures - top 20%
Normalized Range
Aggregate
5.2.2
3 measures
2 measures
1 measure
6
13
41
14
36
48
32
61
88
3 measures
2 measures
1 measure
Individual Ranking
The individual ranking identifies hot spots which are the extreme cases in each individual
measure. Table 5.7 shows the top 10 intersections in each of the aggregate measures, and
Table 5.8 in each of the normalized range measures. In addition, the tables show the
intersections that have already been identified by the combined ranking (CR) using either
aggregate or normalized range measures.
In some cases, the top ranking intersection is easily identified. For example, the top intersection in aggregate running time variability has variability of about 30% higher than
the second-ranked. In other cases, there is no clear threshold to determine which intersections are the extreme cases. For example, it is difficult to discern a single intersection
with the worst performance in the aggregate delay. In this application, the five top ranking
intersections in each measure are selected to be included in the hot spot list, but other
alternatives, such as selecting intersections exceeding a certain threshold, are also valid.
Nine of the intersections selected by individual ranking had already been selected by the
combined ranking of the aggregate or normalized range measures. In addition, there are
two intersections that are in the top 5 in both an aggregate measure and a normalized range
measure.
5.2.3
Hot Spot Intersections and Prioritization
Based on the ranking methodology above, a total of 87 intersections were identified as hot
spots. A list is found in Appendix A. Figure 5-6 shows how these intersections (indicated by
the red points) perform in the aggregate measures, while Figure 5-7 shows their performance
in the normalized range measures. It shows that the intersections that were selected by the
combined ranking of the aggregate performance measures may not necessarily perform the
85
Table 5.7: Top 10 intersections in aggregate measures
(a) Top 10 intersections in aggregate RTV
J6544
J2650
J7852
J6566
J4582
J6596
J4759
J6738
J1759
J6213
Agg RTV
in Agg CR?
2.56
1.75
1.46
1.44
1.39
1.38
1.37
1.34
1.33
1.32
x
x
x
(b) Top 10 intersections in aggregate delay
Agg Delay
J1787
J4759
J2650
J4529
J6301
J4779
J2157
J4721
J2533
J3709
0.81
0.79
0.79
0.73
0.70
0.70
0.70
0.70
0.69
0.69
in Agg CR?
x
x
x
x
x
x
(c) Top 10 intersections in aggregate speed
Agg Speed
J2793
J2116
J2754
J1718
J3552
J2650
J2150
J2157
J3566
J6820
2.93
3.26
3.64
3.75
4.02
4.17
4.22
4.24
4.32
4.41
86
in Agg CR?
x
x
x
x
x
Table 5.8: Top 10 intersections in normalized range measures
(a) Top 10 intersections in normalized RTV range
RTV Range
J4582
J5791
J4739
J5513
J6515
J6522
J3654
J3508
J2208
J4907
0.91
0.88
0.87
0.84
0.84
0.80
0.79
0.79
0.78
0.77
in Range CR?
x
x
(b) Top 10 intersections in normalized delay range
Delay Range
J4572
J4832
J2710
J5537
J3514
J7589
J4609
J3715
J7762
J4632
0.99
0.99
0.93
0.92
0.89
0.88
0.87
0.86
0.84
0.82
in Range CR?
x
x
(c) Top 10 intersections in normalized speed range
Range Speed
J3133
J3576
J3229
J6301
J5110
J5532
J3508
J7581
J3305
J4418
0.98
0.94
0.93
0.92
0.92
0.90
0.89
0.89
0.88
0.88
in Range CR?
x
x
87
50
0.5
Aggregate Delay
40
30
20
0.7
0.6
10
Aggregate Speed (mph)
0.9
0.8
0.4
0.3
0.2
0
0.1
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Aggregate RTV
!
Figure 5-6: Aggregate RTV, delay, and speed - hot spot intersections
worst in terms of the normalized range measures, and vice versa. Therefore, performing the
combined ranking twice, on the aggregate measures and the normalized range measures,
ensures that the critical intersections are selected in terms of those having poor overall
performance and those where only a few buses experience higher variability, delays and
lower speeds.
Figure 5-8 shows the spatial distribution of the hot spot intersections determined by the
ranking methodology. In some areas, there are a number of hot spots intersections that are
very closely located. These areas are good places to start investigating corridor-level issues.
Adding the intersections which perform the worst in only one or two measures may help
reveal longer sequences of problematic intersections, from which hot spot corridors can be
identified.
With 87 intersections identified, it may be difficult to target bus priority efforts to all at
once. One way of prioritizing intersections is to examine them separately for each category
of measures. Another is by choosing the intersections with the highest number of buses per
88
1.0
0.2
0.0
0.0
0.2
0.4
0.6
0.8
Normalized Delay Range
0.8
0.6
0.4
0.2
0.6
0.4
0.0
Normalized Speed Range
1.0
0.8
1.0
Normalized RTV Range
Figure 5-7: Normalized range of RTV, delay and speed - hot spot intersections
89
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W2
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E12
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S5
Legend
^
Hot Spot Intersections
TfL Potential Bus Priority Locations
Figure 5-8: Location of hot spot intersections
hour. Figure 5-9 shows the distribution of the average number of buses per hour passing
through the intersection in the AM peak for the hot spot intersections identified. About
90% of the intersections have at least 30 buses per hour crossing the intersection; this
amounts to roughly at least one bus passing through the intersection every two minutes.
This distribution also may indicate that in some cases the large number of buses through
intersections may also be one of the sources of the problem.
The hot spot intersection with the greatest number of buses per hour is the London Underground Bank Station; approximately 171 buses pass through this intersection per hour
in the AM peak. Figure 5-10 shows a schematic of this intersection. It has twenty highfrequency routes passing through in the AM peak, with frequencies between 6 and 15 buses
per hour. In addition, the intersection has a complex geometry, having 6 legs and a total
of 12 lanes. There is a single bus lane on the northeast-bound approach. Implementing
additional bus priority at this intersection requires a detailed analysis of the impact on
performance for all routes. It may be difficult to implement bus priority measures that
90
14 Number of Intersec/ons 12 10 8 6 4 190 -­‐ 200 180 -­‐ 190 170 -­‐ 180 160 -­‐ 170 150 -­‐ 160 140 -­‐ 150 130 -­‐ 140 120 -­‐ 130 110 -­‐ 120 100 -­‐ 110 90 -­‐ 100 80 -­‐ 90 70 -­‐ 80 60 -­‐ 70 50 -­‐ 60 40 -­‐ 50 30 -­‐ 40 20 -­‐ 30 10 -­‐ 20 0 0 -­‐ 10 2 Number of Buses per Hour Figure 5-9: Total number of buses per hour at hot spot intersections
!
Figure 5-10: Schematic of Bank Station intersection
will benefit the majority of routes passing through. For example, giving signal priority for
one movement will likely cause delays for routes on conflicting movements. It is important
to consider here that the large number of routes passing through the intersection may be
91
the cause of the poor performance of buses. Stops are served by multiple routes, and with
limited capacity at the intersection, a bus likely waits for the preceding bus to leave the
stop in order to serve its passengers. This results in delays for passengers and increased
running times for the bus.
5.3
Analysis of TfL Hot Spots
Transport for London’s current process for identifying hot spots is described in Section
3.1.2. Based on feedback from operators on delays, the percent of lost mileage due to
traffic, and traffic data used to determine areas of low speeds and high delays for general
traffic, their process identified 28 locations across various regions in London. This section
compares TfL’s hot spot locations and characteristics with the hot spots identified through
this research.
5.3.1
Characterizing TfL’s Hot Spots
First, the intersections in TfL’s hot spot locations were identified. Figures 5-11 to 5-18 show
the distributions of all six measures, the total number of buses per hour through the intersection, and the number of accidents, distinguishing between TfL’s hot spots and the rest of
the intersections. The intersections that are within TfL’s hot spot locations vary in terms
of their performance and are not necessarily the worse performing. As mentioned earlier,
the reason that TfL’s hot spots may not be the worst performing is because TfL selected
corridors for potential implementation of bus priority. Buses may experience a deterioration
in performance at the corridor-level, but perform relatively well at intersections.
Figures 5-19 and 5-20 show how TfL’s hot spots (indicated in red) perform in comparison
to the rest of the intersections according to the three aggregate measures and the three
normalized range measures. These figures show that very few intersections in TfL’s hot
spot locations are worst performing according to all three aggregate or all three normalized
range measures.
92
0 Rest of Intersec;ons 93
Aggregate Delay TfL Hot Spots Figure 5-12: Distribution of aggregate delay - TfL hot spot comparison
0.95 -­‐ 1 0.9 -­‐ 0.95 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 Rest of Intersec;ons 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0 -­‐ 0.1 0.1 -­‐ 0.2 0.2 -­‐ 0.3 0.3 -­‐ 0.4 0.4 -­‐ 0.5 0.5 -­‐ 0.6 0.6 -­‐ 0.7 0.7 -­‐ 0.8 0.8 -­‐ 0.9 0.9 -­‐ 1 1 -­‐ 1.1 1.1 -­‐ 1.2 1.2 -­‐ 1.3 1.3 -­‐ 1.4 1.4 -­‐ 1.5 1.5 -­‐ 1.6 1.6 -­‐ 1.7 1.7 -­‐ 1.8 1.8 -­‐ 1.9 1.9 -­‐ 2 2 -­‐ 2.1 2.1 -­‐ 2.2 2.2 -­‐ 2.3 2.3 -­‐ 2.4 2.4 -­‐ 2.5 2.5 -­‐ 2.6 2.6 -­‐ 2.7 2.7 -­‐ 2.8 2.8 -­‐ 2.9 2.9 -­‐ 3 0 0 -­‐ 0.05 Number of Intersec;ons Number of Intersec;ons 450 400 350 300 250 200 150 100 50 Aggregate Running Time Variability TfL Hot Spots Figure 5-11: Distribution of aggregate RTV - TfL hot spot comparison
400 350 300 250 200 150 100 50 0 Rest of Intersec;ons 94
0.95 -­‐ 1 0.9 -­‐ 0.95 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 Rest of Intersec9ons 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 > 40 38 -­‐ 40 36 -­‐ 38 34 -­‐ 36 32 -­‐ 34 30 -­‐ 32 28 -­‐ 30 26 -­‐ 28 24 -­‐ 26 22 -­‐ 24 20 -­‐ 22 18 -­‐ 20 16 -­‐ 18 14 -­‐ 16 12 -­‐ 14 10 -­‐ 12 8 -­‐ 10 6 -­‐ 8 4 -­‐ 6 2 -­‐ 4 0 -­‐ 2 0 0 -­‐ 0.05 Number of Intersec;ons Number of Intersec9ons 500 450 400 350 300 250 200 150 100 50 Aggregate Speed (mph) TfL Hot Spots Figure 5-13: Distribution of aggregate speed - TfL hot spot comparison
250 200 150 100 50 Normalized Running Time Variability Range TfL Hot Spots Figure 5-14: Distribution of normalized RTV range - TfL hot spot comparison
0 Rest of Intersec;ons 95
0.95 -­‐ 1 0.9 -­‐ 0.95 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 Rest of Intersec;ons 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0.95 -­‐ 1 0.9 -­‐ 0.95 0.85 -­‐ 0.9 0.8 -­‐ 0.85 0.75 -­‐ 0.8 0.7 -­‐ 0.75 0.65 -­‐ 0.7 0.6 -­‐ 0.65 0.55 -­‐ 0.6 0.5 -­‐ 0.55 0.45 -­‐ 0.5 0.4 -­‐ 0.45 0.35 -­‐ 0.4 0.3 -­‐ 0.35 0.25 -­‐ 0.3 0.2 -­‐ 0.25 0.15 -­‐ 0.2 0.1 -­‐ 0.15 0.05 -­‐ 0.1 0 -­‐ 0.05 0 0 -­‐ 0.05 Number of Intersec;ons Number of Intersec;ons 250 200 150 100 50 Normalized Delay Range TfL Hot Spots Figure 5-15: Distribution of normalized delay range - TfL hot spot comparison
180 160 140 120 100 80 60 40 20 Normalized Speed Range TfL Hot Spots Figure 5-16: Distribution of normalized speed range - TfL hot spot comparison
500"
Number"of"Intersec;ons"
450"
400"
350"
300"
250"
200"
150"
100"
0"
0"("10"
10"("20"
20"("30"
30"("40"
40"("50"
50"("60"
60"("70"
70"("80"
80"("90"
90"("100"
100"("110"
110"("120"
120"("130"
130"("140"
140"("150"
150"("160"
160"("170"
170"("180"
180"("190"
190"("200"
200"("210"
210"("220"
220"("230"
230"("240"
240"("250"
250"("260"
260"("270"
270"("280"
280"("290"
290"("300"
>"300"
50"
Total"Number"of"Buses"per"Hour"
Rest"of"Intersec;ons"
TfL"Hot"Spots"
Figure 5-17: Distribution of total number of buses per hour - TfL hot spot comparison
1600 Number of Intersec9ons 1400 1200 1000 800 600 400 200 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Number of Accidents per Year Rest of Intersec9ons TfL Hot Spots Figure 5-18: Distribution of number of accidents per year - TfL hot spot comparison
96
80
70
60
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.2
0.3
0.5
0.6
0.8
0.9
4.0
Aggregate RTV
Figure 5-19: Aggregate RTV, delay, and speed - TfL hot spots
97
Aggregate Delay
50
40
30
Aggregate Speed (mph)
20
10
0
0.1
0.4
0.7
1.0
0.6
0.4
0.0
0.2
0.0
0.0
0.2
0.4
0.6
0.8
Normalized Delay Range
0.8
0.6
0.4
0.2
Normalized Speed Range
1.0
0.8
1.0
Normalized RTV Range
Figure 5-20: Normalized range of RTV, delay, and speed - TfL hot spots
98
5.3.2
Comparison
Comparing the intersections in TfL’s hot spot locations with the identified hot spot intersections according to the methodology discussed here, it was found that there are 15
common intersections. The specific ranking method by which they were identified is shown
in Table 5.9. If the aggregate and normalized range combined ranking is extended to the
top 20% of intersections, then the number of intersections in common with TfL increases to
35: 12 are identified by the aggregate combined ranking, and 18, by the normalized range
combined ranking. One explanation for the discrepancy between the hot spots identified
by the ranking methodology and those by TfL is that some of the locations were selected
by TfL because buses experience delays at the corridor level. Delays at the corridor level
are not captured by the performance measures at the intersection level. In some cases, a
number of intersections identified by the ranking methodology are found within these corridor. However, this indicates that buses experience a deterioration in performance through
a corridor partly due to delays at intersections.
Table 5.9: Ranking method by which intersections in common with TfL were identified
Ranking
Combined
Individual
Combined
Individual
Common intersections with TfL
aggregate
aggregate
range
range
6
1
4
4
A closer look at four different intersections may shed light on the reasons for the large
discrepancy between TfL’s hot spot list and the intersections identified by this methodology.
Intersections in both TfL and Ranking Methodology Lists
The Putney Bridge intersection, shown in Figure 5-21 was identified by both TfL and the
ranking methodology as a hot spot. Table 5.10, which presents the intersection performance
measures, indicates that Putney Bridge was identified as a hot spot because it ranks in the
top 10% in the aggregate measures. While this intersection performs relatively well in terms
of the normalized range measures of variability and delay, Putney Bridge has high ranking
99
in the normalized speed range. This indicates that routes which travel through one of the
movements of the intersection have disproportionately lower speeds than the rest of the
routes.
!
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!
!
!
!!!OD!1!
!!!OD!2!
!!!OD!3!
!!!OD!4!
!!!OD!5!
!!!OD!6!
!!!OD!7!
!!!OD!8!
!!!OD!9!
!
!
Figure 5-21: Schematic of Putney Bridge intersection
Table 5.10: Putney Bridge intersection performance measures
Measure
Aggregate RTV
Aggregate Delay
Aggregate Speed (mph)
Normalized RTV Range
Normalized Delay Range
Normalized Speed Range
Number of Buses per hour
Number of Accidents per year
Value
Ranking
1.06
0.69
5.78
0.49
0.46
0.81
87.6
3
68
14
88
498
562
47
271
84
A look at the individual route measures can easily identify these routes, shown in Table
5.11. An OD pair represents the set of stops before and after the intersection that a route
travels through. Routes that share the same stops before and after the intersection belong
to the same OD pair. The table shows that routes which belong to the first OD pair are
experiencing lower speeds. These are the routes that travel northbound on Putney High
Street.
This intersection was probably identified by TfL because of the large delays that all routes
100
Table 5.11: Performance measures of routes through Putney Bridge intersection
Route
Direction
OD Pair
RTV
Delay
Median
Speed (mph)
Percent lost mileage
due to traffic
14
39
85
93
14
337
337
37
37
39
93
430
430
1
2
1
1
2
1
2
1
2
1
2
1
2
1
1
1
1
2
3
4
5
6
7
7
8
9
1.21
1.20
1.29
1.13
0.87
0.78
0.96
1.17
0.95
0.94
0.89
1.54
1.12
0.86
0.87
0.86
0.83
0.52
0.47
0.60
0.76
0.58
0.62
0.62
0.73
0.50
2.39
2.19
2.34
2.27
7.88
8.02
5.17
2.39
7.79
11.50
10.61
4.81
6.02
2.8%
0.9%
2.0%
1.7%
2.8%
2.4%
2.4%
2.3%
2.3%
0.9%
1.7%
3.4%
3.4%
experience through the intersection, due to its proximity near a National Rail station and
the high number of routes that pass through. The ranking methodology was able to identify
these issues.
Another intersection which was identified by both methodologies is the Baker Street intersection. Figure 5-22 presents a schematic of the Baker Street intersection. This intersection
has 19 route-directions passing through. The area indicated by the circle is a hot spot
location identified by TfL; it is the corridor on Baker Street bounded by Marylebone Road
on the north and Oxford Street on the south.
!
!
!
!
!
!
!
!
!
OD!1!
OD!2!
OD!3!
OD!4!
OD!5!
OD!6!
!
Figure 5-22: Schematic of Baker Street intersection
101
Table 5.12 shows how Baker Street performs as an intersection. The aggregate measures
indicate that as a whole, Baker Street is not one of the worst performing intersections.
Although the aggregate speed is low—Baker Street is in the bottom 14th percentile according
to speed—the variability and delay experienced by buses through the intersection rank in the
26th and 32th percentiles respectively. However, examining the normalized range measures
indicates that Baker Street ranks in the worst 3% according to variability, and 2% according
to both delay and speed. It belongs to the hot spot list according to the range measures.
This suggests that examining individual route performance may shed light on the reason
the intersection experiences such large differences in variability, delay and speed among the
routes passing through.
Table 5.12: Baker Street intersection performance measures
Measure
Value
Ranking (out of 2082)
Aggregate RTV
Aggregate Delay
Aggregate Speed (mph)
Normalized RTV Range
Normalized Delay Range
Normalized Speed Range
Number of Buses per hour
Number of Accidents per year
0.813
0.482
7.00
0.71
0.72
0.82
176.0
3
546
669
281
63
41
39
46
84
Table 5.13 summarizes the running time variability, delay, median speed and running time
through the intersection for all route-directions passing through the Baker Street intersection. In addition, the table indicates which OD pair each route-direction belongs to.
Looking at the route-direction with the highest running time variability and delay, and
lowest speed (indicated in grey), they all belong to the first OD pair. In fact, most of the
route-directions in the first OD pair experience the highest variability and delay and lowest
speeds among all the route-directions through the intersection. These are the routes going
southbound along Baker Street. Examining the signal timings at the intersection may explain why routes on this approach experience high variability and delays. It may be that
the phase settings grant more green time to approaches on Marylebone Road (considered
to be the major road) to accommodate high volumes of general traffic.
102
103
2
1
1
1
1
1
1
2
1
1
1
1
1
1
2
2
2
1
2
74
113
82
274
13
189
139
453
27
205
18
74
30
453
205
18
27
2
30
1
1
1
1
2
2
2
3
3
3
3
3
3
4
4
4
4
5
6
OD Pair
1.212
1.053
1.041
0.972
0.468
0.412
0.350
0.940
0.873
0.845
0.823
0.594
0.408
0.996
0.932
0.870
0.840
0.849
0.779
RTV
0.714
0.641
0.662
0.638
0.522
0.452
0.522
0.409
0.309
0.368
0.386
0.200
0.225
0.586
0.537
0.518
0.526
0.405
0.457
Delay
4.83
3.57
3.63
3.68
4.32
4.24
4.12
9.21
10.01
10.67
9.21
20.27
10.14
5.96
6.40
6.18
6.07
5.59
5.96
Median Speed (mph)
shaded grey indicates highest value in measure
Direction
Route
1.75
2.37
2.33
2.30
1.72
1.75
1.80
0.73
0.68
0.63
0.73
0.33
0.67
0.97
0.90
0.93
0.95
3.07
1.17
Median RT (min)
Table 5.13: Performance measures of routes through Baker Street
1.7%
0.7%
2.0%
4.8%
3.4%
3.6%
0.9%
2.3%
1.7%
2.6%
2.5%
1.7%
0.9%
2.3%
2.6%
2.5%
1.7%
1.1%
0.9%
Percent Lost Mileage
due to Traffic
Table 5.13 also indicates that the route-direction with the highest median running time
through the intersection belongs to OD pair 5, which makes a right turn on Marylebone
Road and onto Baker Street. A short field study of this intersection revealed that this phase
only receives about 10 seconds of green time, resulting in higher running time for routes
making this movement through the intersection. Signal settings that grant more green time
to vehicles on Marylebone Road, combined with large bus traffic on Baker Street, manifest
themselves in congested conditions on the Baker Street corridor identified by TfL as a hot
spot. The table also shows the percent of lost mileage due to traffic. There is a large
variation in the percent lost mileage due to traffic for routes belonging to the first OD
pair. Because this is a route-level measure, it is difficult to attribute the lost mileage to the
intersection itself. Looking at individual route measures and at smaller spatial scale can
help reveal issues that the percent lost mileage due to traffic cannot discern.
Both the intersections discussed in this section had a high number of buses passing through,
which may explain their poor performance. In addition, three accidents occurred within 150
meters of each of the intersections in 2012. The distribution of the number of bus accidents
at an intersection indicated that the majority experienced zero accidents in 2012. This may
be because most accidents occur along corridors, where buses are moving at higher speeds.
Examining the number of accidents at the corridor level may be more informative for the
identification of corridor-level hot spots.
Intersection Hot Spots According to the Ranking Methodology Only
The ranking methodology identified a number of intersections that were not identified by
TfL. They include intersections such as the Bank Station intersection, Oxford Circus, Trafalgar Square, and Euston Road with Pancras Road. These are all intersections that are in
close proximity to large rail stations and located in central London. TfL most likely excluded
these intersections because implementing bus priority at such complex, heavily congested
intersections is infeasible. The high level of interaction between general traffic, buses, pedestrians and cyclists makes it unlikely that any bus priority treatment would be cost-effective
and provide benefit to some routes without adversely affecting the rest of the routes.
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Intersection in TfL Hot Spot List Only
Kingsbury Circle was identified by TfL as a potential location for bus priority, shown in
Figure 5-23, but was not identified by the ranking methodology.
!
!
!
!
!
!!!OD!1!
!!!OD!2!
!!!OD!3!
!!!OD!4!
!!!OD!5!
!
Figure 5-23: Schematic of Kingsbury Circle intersection
Table 5.14 shows the intersection performance measures of the Kingsbury Circle roundabout, and Table 5.15 shows the individual route performance measures. The intersection
ranks low in all of the aggregate and normalized range measures, and the individual route
measures indicate that none of the routes experience significantly high variability, delays, or
low speeds. TfL may have chosen this intersection because of its proximity to the Kingsbury
station. A large number of passengers boarding and alighting at this station may significantly increase bus running times near this intersection but because the measures developed
exclude the dwell time at stops, this delay is not captured in the aggregate or normalized
range measures and the intersection is not identified as a hot spot.
5.4
Conclusions and Recommendations
This chapter proposed a methodology for identifying potential locations for bus priority
measures at the intersection level and applied it to the London network. The methodology relies on measures that fall into two categories: those that describe overall intersection
performance—aggregate measures—and those that describe the variation in performance
105
Table 5.14: Kingsbury Circle intersection performance measures
Measure
Aggregate RTV
Aggregate Delay
Aggregate Speed (mph)
Normalized RTV Range
Normalized Delay Range
Normalized Speed Range
Number of Buses per hour
Number of Accidents per year
Value
Ranking
0.65
0.46
7.81
0.30
0.28
0.20
37.1
0
1143
764
438
1244
1293
1489
919
586
Table 5.15: Performance measures of routes through Kingsbury Circle intersection
Route
Direction
OD Pair
RTV
Delay
Median
Speed (mph)
Percent Lost Mileage
due to Traffic
183
183
204
204
79
79
1
2
1
2
1
2
1
2
2
3
4
5
0.80
0.56
0.63
0.66
0.68
0.58
0.56
0.48
0.40
0.48
0.42
0.44
7.80
8.11
7.80
6.91
8.67
7.51
0.1%
0.1%
0.5%
0.5%
0.7%
0.7%
among routes through an intersection—normalized range measures. Performance is measured in terms of the running time variability, delay, and speed experienced by routes
through the intersection for each of the categories. Using the aggregate measures, a combined ranking is performed to identify intersections which are top ranking in all three. A
similar procedure is performed using the normalized range measures. In addition, intersections which are top ranking in each individual measure but have not been identified
by the combined ranking are also included in the hot spot list. The application of this
methodology on the London network identified a list of 87 intersection hot spots for which
the implementation of priority measures may improve performance for the routes passing
through.
A comparison of the hot spot intersections identified by the ranking methodology with the
intersections in TfL’s hot spot locations indicated that a small percentage, about 9% of
TfL’s intersections are identified as hot spots by the ranking methodology.
The methodology presented here relies on Automatic Vehicle Location data to measure
bus performance. In that sense, it has an advantage over TfL’s current method for iden-
106
tifying hot spot locations. While the analysis in this research focused on the AM peak,
it can be easily implemented for the peak period or any hour of the day. This allows for
the comparison of intersection performance throughout the day. It may be that certain
intersections consistently perform badly throughout the day, and therefore the investment
in bus priority measures at these intersections is justified. In addition, the analysis can be
repeated several times throughout the year to monitor performance, and attention should
be directed to intersections where buses are experiencing deteriorating performance. In
addition, once a list of hot spot intersections is identified, it is relatively simple to obtain
the measures of individual route performance through the intersection, because they form
the basis of the intersection measures. An examination of individual route performance can
quickly pinpoint which movements are experiencing the highest deterioration in service and
provide a more detailed idea of the performance of the intersection. Data on the operational
characteristics and surroundings of the intersection can provide tailored solutions for bus
priority.
It should be noted that the methodology does not provide the definitive list of locations
where bus priority should be implemented. Judgment is always important to exclude certain
locations, for example, in central London, because prior studies may have already concluded
that implementing bus priority measures at these locations is too expensive or infeasible.
In addition, a detailed analysis of any intersection identified as a hot spot is needed to
evaluate the most suitable type of priority measure and the impacts on both buses and
general traffic.
One limitation of the methodology is that it excludes the dwell times at stops before and
after the intersection. Intersections where buses travel smoothly through the intersection
but spend large amounts of time serving passengers in the vicinity of the intersection are
not identified. Nevertheless, this methodology provides a way of focusing initial efforts and
can be applied to one borough at a time for a more localized and smaller scale study.
A critical part of any methodology that identifies potential locations for bus priority is
understanding the causes of poor bus performance and evaluating the impact of priority
measures on performance. These issues are addressed in the next chapter.
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Chapter 6
Route Performance Models
To determine the effectiveness of bus priority measures in improving bus travel times and
reliability, it is essential to gain an understanding of the factors affecting route performance.
Bus operators generally have an idea of what these factors are and how they affect service,
but often lack a formal quantitative analysis to validate their beliefs and better inform their
operating decisions. This chapter identifies the major factors affecting route performance
and provides a framework for determining their impact. The goal is to model a number of
route performance measures as a function of the different factors, and identify the factors
that have a substantial influence on performance. Quantification of this impact can also be
useful in informing bus priority measures that can mitigate poor performance.
This chapter uses the London bus network as a case study to develop route performance
models. Section 6.1 presents the measures used to describe route performance. Section
6.2 identifies the major factors believed to affect performance and discusses how they are
quantified for input into the models using the available data. Finally, a number of linear
models are presented in Section 6.3 with an interpretation of the results.
6.1
Measuring Route Performance
A number of measures can be used to monitor route and operator performance, some
of which were introduced in the literature review in Section 2.3. This section discusses
four route performance measures that will be used in the regression analysis: the median
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running time, the running time variability, the median speed, and the percentage of lost
mileage.
6.1.1
Median Running Time, Running Time Variability, and Speed
Running time is the amount of time it takes for a bus to travel along its route. Characteristics of a route’s running time distribution are important indicators of its performance. Two
such characteristics are the median running time and the running time variability. Variability has important implications for resource allocation so it would be useful to quantify
the various contributing factors. More resources are required to operate routes with higher
running time variability; in order to maintain the schedule, operators include slack time at
the terminal, or layover time, to allow for a vehicle that is arriving late from its previous
trip to depart on time for its next trip. Given a particular headway, a route with higher
running time variability will need more layover time and therefore more vehicles to maintain
that headway at a given level of reliability.
Both the median running time and the variability can be calculated at the route-direction
level using Automatic Vehicle Location (AVL) data. For example, in London, the running
time distribution for each direction of a route and for any time period can be easily obtained
using the iBus database. The iBus database is discussed in detail in Section 3.2.1. Using
the departure time from the first stop along the route and the arrival time at the last stop,
the running time of a trip is determined. A set of running time observations is obtained for
a time period, from which the median running time and variability are calculated. There
are a number of measures that describe variability; this chapter uses the normalized mean
spread defined in Section 4.2.1 at the route-direction level. Using a sample of 170 routes in
London, the distributions of the median running time and the running time variability at
the direction level are shown in Figures 6-1 and 6-2. These routes are representative and
cover the entire area.
Another variable of interest that describes route performance is the median speed of a
route. This is determined by dividing the length of a route by its median running time.
The distribution of the speed for the sample of routes is shown in Figure 6-3.
The descriptive statistics of these three variables are shown in Table 6.1.
109
Figure 6-1: Distribution of median bus running time at direction level
Figure 6-2: Distribution of running time variability at direction level
110
Figure 6-3: Distribution of median speed at direction level
Table 6.1: Descriptive statistics of median running time, variability, and speed
Running Time (min)
RTV
Speed (km/h)
61.6
15.0
15.6
103.3
0.190
0.048
0.094
0.388
13.1
1.9
8.1
21.8
Mean
Standard Deviation
Minimum
Maximum
6.1.2
Percent Lost Mileage
Percent lost mileage is a measure that London Buses uses to monitor performance and calculate payments to operators. It is defined as the percentage of scheduled revenue vehicle
miles that are not operated in the time period of interest. Operators are required to provide
a reason, or cause code, for route miles that are not run. These cause codes include staffing
shortages, mechanical issues, traffic, and iBus reporting errors, and are recorded manually
by the operator and entered into TfL’s iBus database. Lost mileage is categorized as either
deductible or non-deductible. Deductible lost mileage refers to situations where the operator is responsible for a whole or part of a trip being cancelled, and is therefore penalized as
111
stipulated in their contract. These typically include situations with crew shortages or bus
maintenance issues. Non-deductible lost mileage refers to situations where the trip cancellations are beyond the operator’s control, and include traffic congestion or malfunctioning
iBus units. Because of the way this reporting system is set up, data on lost mileage may be
susceptible to errors because operators are penalized for certain situations and therefore,
may not be completely accurate in assigning cause codes for lost mileage.
Nonetheless, using percent lost mileage due to traffic to measure route performance may
be informative. It would be useful to evaluate the extent to which it is explained by traffic
conditions, operator characteristics, route attributes, etc. The percent of lost mileage due
to traffic was obtained for all of London’s bus routes in the period from September 19
to October 10, 2012. Figure 6-4 shows the distribution of lost mileage due to traffic for
these routes, and Table 6.2 presents the distribution’s descriptive statistics. The average
percentage of lost mileage due to traffic was calculated using only weekdays. It should be
noted that this measure is provided for an entire route and day; this aggregation makes it
difficult to separate out effects due to certain direction characteristics or varying levels of
traffic congestion throughout the day.
Table 6.2: Descriptive statistics of percent lost mileage due to traffic
Mean
Standard Deviation
Minimum
Maximum
6.2
1.6%
1.1%
0.0%
5.0%
Factors Affecting Route Performance
Identifying the factors that affect route performance and obtaining the data to quantify
these factors are the major challenges in developing these models. Producing an exhaustive
list of factors may be futile if the data requirements to estimate the model are large. This
section first presents a list of potential factors grouped into categories, then discusses in more
detail those for which data was available and were used in the statistical analysis.
Table 6.3 provides a list of factors that affect route performance grouped into several categories. The first category includes factors that describe a route’s environment as it operates
112
Figure 6-4: Distribution of percent lost miles due to traffic
in mixed traffic. A route’s performance is likely to be affected by congestion, accidents
and roadworks, and road-side activities such as parking and loading and unloading vehicles.
Intersections also significantly affect a route’s performance, and the impacts can vary depending on an intersection’s characteristics such as its geometry and signal settings. Buses
also share the road with pedestrians and cyclists, and the interaction of a bus with these
modes may be reflected in its performance.
The second category includes characteristics that are inherent to each route. These include
the route length, the number of stops it makes, any priority measures available along the
route, and its complexity. A route’s complexity may arise because of the area in which
it operates (in a suburban environment versus the more congested downtown area) or the
attributes of the vehicle used to operate the route.
Ridership attributes have a direct impact on route performance. Variability in demand
affects dwell times. The number of passengers boarding and alighting varies throughout the
day, and as a result, successive trips may experience varying dwell times.
113
Operator behavior is another important factor that affects performance. Operators may
provide strict instructions to their drivers regarding safety and driving attitude, while others
may be more lax. The control strategies employed play an important role as well. Other
external factors, such as the weather, may represent changes in performance not captured
by other factors.
Table 6.3: Factors affecting route perfomance
Category
Factor
Operating Environment
Traffic congestion
Accidents and roadworks
Road-side activities
Intersection characteristics
Pedestrian/cyclist interactions
Route Characteristics
Route length
Number of stops
Priority measures
Route complexity
Ridership
Boardings, alightings, and variability
Operator
Driver behavior
Control strategies
External
Weather
The availability of data on all of the factors listed was limited. However, data for the
factors which were identified as the most important for analysis was obtained from TfL.
These factors are traffic, accidents, intersection characteristics, route length, the length of
bus lanes on a route, the number of boardings and the operator of a route; they are discussed
in more detail in the following sections.
6.2.1
Traffic
Traffic congestion is expected to have a significant impact on route performance. The
Integrated Transport Network (ITN) is an Ordnance Survey dataset that contains details
on Great Britain’s road network. It contains information such as the road class, the road
geometry (single carriageway, dual carriageway, etc.), road names and routing information.
The ITN for London was obtained and consists of over 76,700 links covering about 6,000
km. Trafficmaster collects observations on the link journey time using the movements of
114
GPS-equipped vehicles. This data is provided in 15-minute periods for each day and each
link, and was obtained for the three-week analysis period in September and October. More
detail on this dataset can be found in Section 3.2.2.1
Median Traffic Travel Time
Obtaining the traffic travel time along each route involved first mapping London’s bus
routes to the ITN links and then finding the sequence of links that a bus route uses. The
average link journey time for each link in each 15-minute period is calculated using the
Trafficmaster data from weekdays in the analysis period. For a private vehicle starting its
trip at a certain time, say 7:00, at the beginning of the route, the total traffic travel time
is calculated by summing the average link journey times over the sequence of links. For
each link in the sequence, the average journey time used in the summation corresponds to
the time period in which the vehicle is assumed to be on that link. For example, if the
travel time for a trip starting at 7:00 to reach a link in the link sequence is greater than 15
minutes, then the average link time used to find the total travel time is the link travel time
for the 7:15 to 7:30 time period. This approach captures the time experienced by a vehicle
along a path more accurately.
Using the above procedure, for each route and direction, the total traffic travel time for
trips starting at 15-minute increments in the AM period from 7:00 until 9:30 can be found.
This results in a distribution of travel times for private vehicle trips starting in the AM
peak from which the median traffic travel time can be calculated and used as a variable
describing traffic conditions in the statistical analysis.
Figure 6-5 shows the relationship between the median bus running time and the median
traffic travel time along the same path taken by a bus route. As expected, the travel time
for general traffic is shorter than bus running times. This is because private vehicles do
not make stops to pick up passengers and travel at generally higher speeds. In London, the
median travel time for traffic is 25% shorter than the running time of buses for the sample
of 170 routes used.
Another measure of the central tendency of traffic travel times which does not take into
1
All traffic information in this thesis is derived from data provided by TrafficMaster obtained from vehicles
fitted with GPS devices, and produced by TfL Network Performance and Traffic Analysis Centre.
115
Figure 6-5: Median traffic travel time vs. median bus running time
account the movement of a vehicle along the links is the average AM peak traffic travel time.
This is determined by first calculating the average link journey time using all 15-minute
time periods in the AM peak, then summing the average journey times of all links along a
route.
Congestion Index
Another indicator of congestion can be defined by comparing the average traffic travel times
during the peak period of interest with the free-flow travel time. The free-flow travel time
is determined by finding the 15th percentile of the travel times during the off-peak period.
The distribution of this indicator is shown in Figure 6-6. In general, traffic travel times
during the AM peak are between two to three times higher than in the off-peak period.
The method for calculating the congestion index in this research may have overestimated
congestion in the AM peak. A report published by Transport for London (2011b) suggests
that congestion, calculated as the excess travel rate (in min/km) above the travel rate in
uncongested conditions, ranged from 2.1 to 2.3 min/km in 2007 to 2009.
116
Figure 6-6: Distribution of congestion index
Variability of Traffic Travel Time
An important aspect of traffic conditions that may have an impact on route performance
is the variability of traffic travel times. Traffic travel times may vary from day to day
because of unusual events, such as accidents, roadworks or inclement weather, or because
of differences due to the day of the week. It is expected that more variable traffic travel
times contribute towards higher running times or variability for a route.
Traffic travel time variability is measured by the corresponding variance. For the purposes
of this thesis, it is assumed that link travel times are independent and the route travel time
variance is calculated as the sums of the link variances. Equation (6.1) is used to calculate
the variance of traffic travel time along route r.
σr2 =
X
σl2
(6.1)
l∈L
where L is the set of links along which route r travels, and σl2 is the variance of traffic travel
117
Figure 6-7: Traffic travel time CV vs. bus running time CV
times on link l.
Figure 6-7 shows the relationship between the coefficient of variation (CV) of traffic travel
times and that of bus running times. While Figure 6-5 indicated a strong positive linear
trend between traffic and bus median travel times, the trend is not as strong between the
coefficients of variation of traffic and bus travel times. In general, buses experience more
variability than private vehicles traveling along the same route. Much of this variability
may be due to the variability in dwell times, as the number of passengers boarding and
alighting at each stop may change significantly from one bus trip to another.
6.2.2
Intersection Characteristics
The time spent by a bus at an intersection can make up a large portion of its overall running
time. A number of measures can be used to capture the number and characteristics of the
intersections that a route passes through.
118
Intersection Density
The density of intersections is measured as the total number of intersections that a route
passes through divided by the route length. It is expected that a higher density will reflect
negatively on route performance, as buses generally incur delays at intersections.
Total Delay at Intersections
The delay at an intersection is quantified by comparing the median running time through
the intersection with the free-flow running time. The median running time is defined as the
50th percentile of a set of running time observations during the time period of interest, for
example the AM peak. The free-flow running time is defined as the 15th percentile of a set of
running time observations during an off-peak period, usually from 22:00 to 5:00. The total
intersection delay for a route is the sum of the delays encountered at all the intersections
along the route. In order to make consistent comparisons between routes, a more suitable
measure may be the delay per unit length, found by dividing the total delay by the route
length, to take into account that longer routes may pass through more intersections, and
therefore have higher total delay, if the measure being explained is the median running
time.
Intersection Performance Measures
The intersection performance measures developed in Chapter 4 were used to classify the
intersections that a route passes through as a hot spot or not. It is important to capture
the fact that routes passing through intersections that perform badly may be impacted
more negatively than routes which do not pass through such intersections. The effect is
captured by the number of intersections that a route passes through that belong to the worst
quintile according to some measure. Using the measures developed in Chapter 4, this means
determining the number of intersections in the top 20th percentile according to running
time variability or delay, or in the bottom 20th percentile according to speed. Defining
these quantities as a percentage of the total number of intersections a route passes through
provides a normalized measure. In addition, determining the number of intersections in the
second quintile can be used to classify intersections whose performance is not as worse as
119
those in the top quintile, but can still be considered as having a detrimental effect on route
performance.
6.2.3
Route Characteristics
Route Length
Longer routes may be more difficult to operate than shorter routes; as a result, it is expected
that longer routes increase the variability in running times.
Total Length of Bus Lanes
Priority measures aim to decrease bus journey times and improve service reliability. Therefore, routes with more priority measures, such as bus lanes and priority signals, are expected
to have lower median running times and variability than those that operate in mixed traffic
for most of their route. Spatial data on bus lanes in London was obtained and mapped to
the corresponding routes to obtain a measure of the total length of bus lanes on a route.
This quantity was represented as a percentage of total route length for input into the linear
regression model estimation.
Figure 6-8 shows the relationship between the coefficient of variation (CV) of bus running
times of a route and the length of bus lanes as a percentage of total route length. Although
the correlation is not a strong one, the figure indicates that as the percentage of bus lanes
increases the variability of running time decreases.
A measure of the level of occupancy of bus lanes is also considered. This measure is
determined by finding for a given route, the percentage of bus lanes it uses that are heavily
used by other bus routes, and those that are lightly used. In this analysis, bus lanes on
which more than one route traveled are considered heavily used, but this definition can be
adjusted.
120
Figure 6-8: Bus running time CV vs. percentage of bus lanes
Ridership
Ridership levels are related to the headway on a route, but have a direct impact on route
performance. High levels of ridership and large variability in headways lead to longer dwell
times. This results in unreliable service in terms of both increased running times and
variability.
The ridership on a route was measured in terms of the average number of boardings per
trip in the AM peak, calculated by dividing the total number of boardings by the number
of trips in the AM peak. The total number of boardings on a route in a time period is
recorded in TfL’s Electronic Ticketing Machine (ETM) database, and the total number of
trips in the same time period can be calculated from iBus. The distribution of the average
boardings per trip for the sample of routes is shown in Figure 6-9.
121
Figure 6-9: Distribution of average boardings per trip
6.2.4
Operator Behavior
Operators differ in their management styles and service delivery approach. As a result,
routes of similar characteristics but managed by different operators may exhibit widely
varying performance.
6.2.5
Number of Bus Accidents
It is hypothesized that routes with a higher number of accidents along a route may be
an indicator of other underlying causes that may impact the performance of the route.
Information on accidents in which only buses were involved and occurred within 40 meters
of a route were identified from the 2012 road accident database, described in Section 3.2.3.
This statistic provides a measure of the number of bus accidents in a year along each
route.
122
6.3
Model Specification and Analysis of Results
Using the variables defined in the previous section, a number of models were estimated
using ordinary least squares regression. Bus median running time, running time variability,
speed and percent lost miles due to traffic are used as the dependent variables. The goal
is to develop models that explain the variability in the above measures as a function of
various independent casual variables that are expected to have a strong influence on the
dependent variable. The independent variable should also influence the dependent variable
in the direction that aligns with our a priori hypotheses. This section presents four model
estimations and discusses the results.
6.3.1
Models of Median Speed at the Direction Level
The median bus running time, median speed and the running time variability can be calculated at the route-direction level. Models that use these performance measures as the
dependent variable will use data measured at the route-direction level as well. A number
of descriptive statistics of the variables discussed in the previous section are presented in
Table B.1 in Appendix B using 340 observations, two for each route. These statistics are
helpful in estimating and validating models by providing a quick summary of the sample
and giving a sense of the expected order of magnitude of coefficients.
A linear model was specified with median speed as a function of the congestion index
(CongestionIndex), the average boardings per trip per km (Boardings/Length), the intersection density (IntersectionsDensity), the percentage of speed hot spot intersections
(PercentSpeedHotSpots), and the percentage of running time variability hot spot intersections (PercentRTVHotSpots), percentage of intersections with speed in the 20th to 40th
percentiles (PercentSpeedHotSpots2), the percentage of heavily-used bus lanes (HeavyBusLanes/Length), and the percentage of lightly-used bus lanes (LightBusLanes/Length). The
regression results for this model are presented in Table 6.4.
The signs of all the coefficient estimates agree with the a priori hypotheses. As congestion
increases, route speeds decrease. A higher number of boardings per km means higher dwell
times, and as a result larger overall running times and reduced speeds. The coefficients
that relate to intersection characteristics indicate that as the intersection density and the
123
Table 6.4: Estimation results of median speed model 1
(Intercept) (km/hr)
CongestionIndex
Boardings/Length (boardings/km)
IntersectionsDensity (intersections/km)
PercentSpeedHotSpots (%)
PercentSpeedHotSpots2 (%)
PercentRTVHotSpots (%)
HeavyBusLanes/Length (%)
LightBusLanes/Length (%)
Estimate
Std. Error
t-value
Pr(>|t|)
23.379
-2.152
-0.062
-1.042
-0.034
-0.024
-0.018
-0.009
0.037
0.890
0.329
0.038
0.238
0.007
0.009
0.006
0.005
0.046
26.266
-6.532
-1.616
-4.386
-5.127
-2.748
-3.095
-1.595
0.793
2.00E-16
2.51E-10
0.10706
1.56E-05
5.06E-07
0.00633
0.00214
0.11174
0.42847
***
***
***
***
**
**
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.396 on 325 degrees of freedom
Multiple R-squared: 0.4906, Adjusted R-squared: 0.478
F-statistic: 39.12 on 8 and 325 DF, p-value: < 2.2E-16
number of speed and running time variability intersection hot spots increase, bus speeds
also decrease. In addition, the coefficient for the percentage of speed hot spots in the second
quintile is negative, but less, in absolute value, than the percentage of speed hot spots in
the first quintile. It is expected that intersections which perform not as worse will have less
of an adverse effect on the speed than the worst performing.
The coefficients for the bus lane variables indicate that as the percentage of bus lanes which
are utilized by only one route increases, speeds increase. However, as bus lanes become
more congested (when used by many other routes), buses become adversely affected and
their speeds decrease. The statistical significance of the coefficients is very high and the
overall fit, with an adjusted R2 of 0.48, is moderate.
The magnitude of the coefficients indicates the incremental change in the median speed
due to an increase of one unit of the independent variable, all else equal. An increase of
one unit of the congestion index decreases bus speeds by 2 km/hr. An additional boarding
per km decreases speeds by about 0.06 km/hr. This means that for a bus traveling at an
average speed of 12 km/hr, the decrease in speed due to an additional boarding per km
corresponds to a boarding time of about 1.5 seconds per passenger. An extra intersection
per km decreases speeds by about 1 km/hr. This suggests that intersections are critical in
improving performance.
Similar models were estimated using the median running time as the dependent variable;
124
however, the interpretation of the impact of the various factors was not as straightforward
and always consistent with expectations. The models that use speed as the explanatory
factor make it easy to generalize the results across routes. The change in speed as a result of
a change in one of the independent variables has the same implications for all routes.
Similar models were estimated using the running time variability as the dependent variable,
but they did not produce coefficient estimates that were consistent with a priori hypotheses
or the linear fit was not as high as models estimated using the median speed as the dependent
variable.
6.3.2
Models of Percent Lost Mileage due to Traffic at the Route Level
Unlike bus running times and variability, the percent of lost mileage due to traffic is reported at the route level. Models estimated with this measure as the dependent variable
use factors calculated at the route level as well. The descriptive statistics of the variables
used to estimate these models are presented in Table B.2 in Appendix B using 193 observations.
A linear model of percentage of lost mileage due to traffic was specified as a function of the
congestion index (CongestionIndex), the variance of traffic travel times (VarianceTT), the
percentage of bus lanes (PercentBusLane), and the intersection density (IntersectionDensity). The estimation results are shown in Table 6.5.
Table 6.5: Estimation results of percent lost mileage due to traffic model 1
(Intercept)
CongestionIndex
VarianceTT
PercentBusLane
IntersectionDensity
Estimate
Std. Error
t-value
Pr(>|t|)
-1.865
1.200
0.047
-0.357
0.016
0.738
0.289
0.020
0.755
0.222
-2.528
4.159
2.335
-0.473
0.073
0.0123
4.86E-05
0.0206
0.6367
0.9419
*
***
*
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.024 on 188 degrees of freedom
Multiple R-squared: 0.1348, Adjusted R-squared: 0.1164
F-statistic: 7.323 on 4 and 188 DF, p-value: 1.676E-05
The signs of the coefficients are as expected and suggest that increased congestion, higher
variance of traffic travel times, and higher intersection density increase the percent of lost
125
mileage due to traffic, while higher percentage of bus lanes decreases the percent of lost
miles due to traffic. The coefficients for traffic conditions are statistically significant—the
t-value for congestion index is especially high—but the fit of the model, with an adjusted R2
of about 0.12, is poor. The coefficients for the percentage of bus lanes and the intersection
density are statistically insignificant, but are included in the model to test their effect.
Using the delay per km instead of intersection density produced similar results.
Another model was specified which uses, in addition to route variables, operator-specific
dummy variables. One operator must be excluded in order to estimate the remaining
coefficients and is considered as the base. The estimated values of the dummy variable
coefficients can be interpreted relative to the excluded operator. In this model, Abellio
London was arbitrarily chosen as the base operator. The results of the regression are shown
in Table 6.6.
Table 6.6: Estimation results of percent lost mileage due to traffic model 2
(Intercept)
CongestionIndex
VarianceTT
PercentBusLane
IntersectionDensity
OpArrivaKentThameside
OpArrivaLondonNorth
OpArrivaLondonSouth
OpArrivaTheShires
OpCTPlus
OpDocklandsBuses
OpEastLondon
OpLondonCentral
OpLondonGeneral
OpLondonSovereign
OpLondonUnited
OpMetrobus
OpMetroline
OpMetrolineWest
OpSelkent
OpTowerTransit
Estimate
Std. Error
t-value
Pr(>|t|)
0.266
0.864
0.052
-0.458
-0.0856
-2.483
-1.259
-1.160
-2.046
-2.698
-0.915
-1.351
-1.082
-1.016
-1.220
-1.136
-1.955
-1.098
-0.895
-1.276
-0.662
0.89714
0.312
0.0212
0.77904
0.230
1.04025
0.36787
0.3905
0.77276
1.03419
0.65792
0.39763
0.38691
0.34611
0.57782
0.37669
0.49453
0.3453
0.45294
0.43594
0.42942
0.297
2.768
2.464
-0.589
-0.371
-2.387
-3.423
-2.971
-2.648
-2.61
-1.391
-3.4
-2.799
-2.937
-2.112
-3.018
-3.955
-3.18
-1.977
-2.928
-1.542
0.766926
0.006247
0.014695
0.556583
0.711104
0.018048
0.000772
0.003383
0.008829
0.009852
0.166008
0.000834
0.005705
0.003764
0.036099
0.002921
0.000111
0.001742
0.049602
0.00386
0.124916
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9847 on 175 degrees of freedom
Multiple R-squared: 0.259, Adjusted R-squared: 0.1743
F-statistic: 3.058 on 20 and 175 DF, p-value: 4.038E-05
126
**
*
*
***
**
**
**
***
**
**
*
**
***
**
*
**
The signs of the coefficients indicate that higher congestion, higher variability in traffic
travel times, and larger delays at intersections increase the percent of miles lost due to
traffic. The model also suggests that as the percentage of bus lanes increases, the percent
of lost mileage due to traffic decreases. The coefficient for the congestion and the variance
of traffic travel times is statistically significant and the estimates for the operator dummy
variables are significant as a group. The statistical significance for the coefficients of percent
of bus lanes and the intersection delay per km are low but are included to test their effect and
compare with the previous model. Although the fit of the model improved, an adjusted R2 of
about 0.17 is indicative of poor linear fit. This suggests that a large part of the variability
in the data is still not explained by the variables used in the model. The magnitudes
of the coefficients indicate that the operator-specific variables have the greatest effect on
the lost mileage due to traffic, followed by the congestion index, but in a much lower
proportion.
For the routes included in this model, the coefficient estimates for the operator dummy
variables suggest that all else equal, Arriva Kent Thameside and CT Plus operate with
the lowest lost mileage due to traffic compared to the base operator Abellio London, while
Tower Transit operates closer to Abellio but still better. While this model suggests that
the operator of a route has an impact on the amount of lost mileage due to traffic, it
is important not to draw any definite conclusions concerning individual operator effects.
This model does not include variables describing the complexity of routes, the areas they
operate in, or control strategies employed, which may be more useful in characterizing
operator behavior.
6.4
Summary and Conclusions
This chapter aimed at identifying the factors that affect route performance and quantifying
their impacts. Route performance was described using measures that characterize the running time distribution, namely the median running time, the variability in running times,
and the speed. In addition, the analysis focused on a measure that TfL currently uses to
assess route performance, the percentage of lost mileage due to traffic.
Although producing an exhaustive list of factors that affect route performance is challenging
127
and may not have much practical application, the analysis reveals the difficulty in achieving
reliable service in a complex bus network such as London’s. There will always be factors
that lead to poor route performance, such as driver behavior, route complexity or weather,
which cannot be practically captured. This research defines measures to describe a number
of factors which have not been previously addressed in the context of the London bus network. Traffic conditions have always been cited as being significant contributors to route
performance. Using Trafficmaster data that measures the average link journey time of private vehicles on London’s road network, the average traffic travel time along a route, the
variance of traffic travel times, and a congestion index were determined. This research also
defined measures that describe characteristics of intersections that a route passes through,
such as the total delay at intersections and the number of intersections with high variability, high delays, or low speeds. In addition to variables describing traffic conditions and
intersection characteristics, variables describing route attributes, ridership, and the number
of bus accidents along a route were also considered in the linear regression analysis.
Several models were estimated with the goal of detecting patterns of operating environment
and route characteristics that lead to higher or lower speeds and percent of lost miles due to
traffic. These models were estimated using a relatively large sample of London bus routes.
It was found that higher traffic congestion, higher ridership, higher intersection density,
and higher number of intersections with high variability and low speeds all contribute to
decreased speeds. Bus lanes utilized by one route tend to decrease speeds. Models that
related the percent of lost mileage due to traffic as a function of these variables were also
estimated. The results indicate that lost mileage due to traffic is affected by traffic conditions, the extent of bus lanes along a route, and the delay at intersections. In addition,
operator-specific effects are significant explanatory factors of the percent of miles lost due
to traffic. However, the goodness of fit of the models was not high. This suggests that
there are other factors not considered here that are driving percent lost mileage due to
traffic.
These models are useful in suggesting implications regarding the implementation of bus
priority measures. The models indicated that traffic congestion and delays at intersections
are significant contributors to increased running times. This, in turn, suggests that bus
priority measures such as bus lanes, signal priority, and queue jumps at intersections may
128
be effective in improving running times and reliability. On the other hand, other factors
such as ridership, which also contribute to increased running times, cannot be targeted by
bus priority measures in the same manner.
129
Chapter 7
Conclusion
Given the benefits that may be gained by implementing bus priority measures, developing a
systematic approach that can identify strategic locations for providing priority treatment is
an important first step in implementing effective bus priority strategies. This is especially
pertinent in a city such as London, which has a dense, heavily-used bus network and whose
road network is facing increasing pressure to accommodate cyclists and pedestrians. This
thesis improves the understanding of bus performance and the causes affecting performance
in several ways. First, it develops a set of measures that can be used to capture the performance of buses through an intersection, using Automatic Vehicle Location data. Second, it
develops a methodology based on such measures that categorizes intersections with the goal
of identifying a subset of intersections that may warrant bus priority treatments. Finally,
the research connects the sources contributing to increased route running time to route
performance and discusses the implications regarding bus priority.
7.1
7.1.1
Summary
Measuring Intersection Performance for Bus Services
A number of priority measures, such as transit signal priority and queue jump lanes, can be
implemented at intersections to reduce the running time and service unreliability of a bus
as it travels through an intersection. The running time of a route through an intersection
130
is affected by a number of factors. Signal timing phases, intersection geometry, general
traffic volumes, and the interactions with pedestrians and cyclists all contribute to increased
running times and delays. It is important to develop measures that capture these sources of
service unreliability for individual routes through an intersection and additionally, provide
measures that describe intersection performance overall.
Running time variability describes the uncertainty of the running times through an intersection, which in turn affects passenger’s journey times and the route’s resource requirements.
The measure used to describe the running time variability of a route through an intersection is based on the metric developed by Sánchez-Martı́nez (2012). Running times vary
randomly within time periods, so an aggregation method was developed to address this issue
by measuring variability separately in successive, short, overlapping time periods within the
time period of interest (such as the AM peak), and reporting the mean variability across
the short time periods. The research applied this method of determining running time
variability, but using running times at the segment level through the intersection.
The delay of a route through an intersection describes the increase in travel times compared
to travel times during a free-flow period, usually off-peak hours. This is important to capture
because it provides a benchmark for the typical running times that can be realistically
achieved. The measure of delay developed compares the free-flow speed of a route and its
median speed as it crosses an intersection. It is normalized by the free-flow speed to provide
an index and allow for consistent comparisons across routes.
The median speed of a route through an intersection is another measure considered to
describe intersection-level performance. This measure captures the impact of intersection
geometry and signal phasing and potential benefits to the passenger experience, as improved
speeds through the intersection translate into shorter journey times for passengers.
Intersection-level performance measures were developed that combine the performance of
individual routes into a single metric. An aggregate metric characterizes the average performance of an intersection by weighing individual route performance by the number of trips
made by the route through the intersection. The variation among routes through the same
intersection was measured by determining the range in the route performance measures. In
addition, two measures that describe causes or consequences of poor bus performance were
developed: the total number of buses per hour passing through an intersection, and the
131
number of accidents per year within the vicinity of an intersection.
Intersection characteristics were analyzed using for a total of 2,082 intersections in London,
using AVL data for a three-week period between September 19 and October 10, 2012.
The analysis focused on the AM peak period, which was defined as 7:00 to 9:30. Visual
representations of these intersection characteristics make it easy to get a general sense of
where intersection performance is the worst. Quintile maps were produced for each of the
measures that indicated which quintile an intersection belonged to using a color range of
green (best) to red (worst). As expected, these maps showed that intersections belonging
to the worst quintile are generally located in central London or along major roads.
7.1.2
Identification of Hot Spots
A methodology for identifying potential locations for bus priority measures at the intersection level was developed and applied to the London network. The methodology uses the
intersection-level performance measures developed in Chapter 4: the aggregate measures
and the normalized range measures of running time variability, delay, and speed. Each
category of measures has different implications in terms of the intersection problems it
characterizes and the possible solutions for improving bus performance. Aggregate measures identify intersections that require more extensive interventions, while normalized range
measures identify movement-specific issues and require more localized solutions. The first
step of the methodology involves a combined ranking approach. The intersections which
are top ranking in all three aggregate measures and those which are top ranking in all three
normalized range measures are identified as hot spots. Next, intersections which are not
selected based on the combined ranking but are still the worst performing in terms of any
individual measure are tagged as extreme cases and included in the hot spot list. Finally,
the intersections that have the highest number of buses passing through can be selected
first for further study.
The application of this methodology on the London network identified 87 intersection hot
spots for which the implementation of priority measures may improve performance for the
routes passing through. The location of the hot spots varied across London, but in a
few areas several hot spots were closely located. These areas may be starting points for
132
investigating corridor-level issues.
A comparison of the hot spot intersections identified by the ranking methodology with the
intersections in TfL’s hot spot list provided important insight into both methodologies. TfL
employs a level of judgement and subjective reasoning, not present in the ranking methodology, to exclude certain locations in which implementing bus priority is too expensive or
infeasible. In addition, the TfL methodology focused on identifying corridors, rather than
intersections, and uses operator feedback as well. If intersections along a corridor perform
relatively well overall, but the coordination of a sequence of intersections results in poor
performance for buses, this is not identified by the ranking methodology. On the other
hand, once an intersection is identified by the ranking methodology, examining individual
route performance and gaining detailed insight into the interaction of routes through the
intersection becomes straight-forward.
7.1.3
Route Performance Models
Understanding the factors that determine typical running times can help service planners
predict the travel time savings and the changes in resource requirements following the implementation of bus priority measures. Traffic congestion is one of the significant contributors
to increased running times. Several aspects of traffic conditions were quantified: the average
traffic travel time along a route, the variability in traffic travel time, and a measure of traffic
congestion comparing peak period travel times to those in the off-peak. Traffic conditions
were quantified by determining the average time required by private vehicles to travel along
the route. Using a sample of 170 routes in London and an AM peak analysis period, it was
found that the average travel time by private vehicles along the route is about 25% lower
than the median running time of buses. In general, buses experience more variability than
private vehicles traveling along the same route. The congestion index was defined as the
proportion of the average traffic travel time during the AM peak to the travel time time
during the night off-peak period.
Other factors analyzed include characteristics of the intersections that a route passes through,
such as the intersection density and the total delay at intersections. In addition, the intersection performance measures developed in this research were used to quantify the conditions
133
of intersections along a route by determining the percent of a route’s intersections that
have variability, delay and speed in the worst 20th percentile of all intersections. Route
characteristics, the total length of bus lanes, and ridership levels were also analyzed.
A number of linear models were estimated to explore general patterns of route performance
measures using the sample of bus routes in London. The performance measures considered
were the median speed and the percent of lost mileage due to traffic, a measure used by
TfL to monitor route performance and as input into their process for identifying locations
for bus priority. Results suggested that congestion, delay at intersections, and congested
bus lanes decrease median speed. In addition, as the percent of intersections with speed
and variability in the worst 20th percentile increases, speed decreases. Models that try to
explain the percent of lost mileage due to traffic indicated that this measure varies greatly
from route to route. Although traffic conditions affect the percent of lost mileage due to
traffic along a route, operator-specific effects are also useful explanatory factors.
Based on the results in this thesis, a number of conclusions emerge:
• The methodology proposed and tools developed provide the basis for quantitative
evaluation of intersection performance from the bus perspective.
• The coordination between bus and traffic entities at Transport for London can provide
a number of benefits. The models estimated in this research showed that congestion
and intersection delays are contributors to deteriorating route performance. With
enhanced information on traffic conditions, bus service providers can identify and
design strategies based on objective quantitative analysis. On the other hand, traffic
entities can utilize bus data to test adjustments to the road network and signal timings
and evaluate the impacts on bus performance, in addition to general traffic.
7.2
Limitations and Future Work
There are several ways to extend the analysis performed in this research and provide additional insight into the approaches used to identify locations for bus priority.
First, the research focused on identifying locations for bus priority measures at the intersection level. In the methodology developed, not all factors causing performance deterioration
134
were taken into account due to the data limitations. Estimating the potential savings
that can be realized at the intersection with the implementation of bus priority measures
will greatly improve the methodology. Such a methodology will be able to distinguish the
intersections where buses experiences delays and there is a potential for travel time savings from those where performance is bad for buses yet it is unlikely there will be any
reduction in running times because of for example, intersection capacity constraints or high
volume-to-capacity ratios. The methodology could be improved by considering the number
of passengers through an intersection explicitly as means of prioritizing intersections. Those
with higher passenger loads could be targeted first because of the benefits gained by such
a large number of passengers.
Another extension of the current work is the identification of corridor-level hot spots. While
many of the causes that affect the running time of buses through intersections are still
present at the corridor level, there are several others that are unique to corridor performance.
Examples include the interaction among intersections (e.g. traffic signal plans); road-side
activities such as parking; and interactions with pedestrians and cyclists. In addition,
available data on bunching can be better utilized at the corridor level, with other measures,
in a way similar to the intersection analysis to identify the corridors where this frequently
occurs as those warranting priority measures. The software tools developed in this thesis
can be extended to process the measures of interest at the corridor level.
The regression models estimated in this research to explain performance at the route level
included important factors. However, only some portion of the variability in the data
could be explained. Some improvement could result by using less aggregate ridership data.
Rather than using the average boardings at the route level, boardings and alightings at
the stop-level can be used to capture the effect of the dwell time at the busiest stops on
the running time. The origin-destination inference tool developed by Gordon (2012) can
be used to obtain this level of ridership data. Other sources of data which may provide
some improvement to the model estimation include more detailed data on priority measures.
Factors such as whether intersections have transit signal priority or not can be explored.
In addition, different characteristics related to traffic, ridership and location within the
city, of the different segments along a route, could be informative. The analysis conducted
in this thesis did not include any data about roadworks and disruptions, although such
135
information, if available, could explain a lot of the observed variability.
136
Appendix A
List of Hot Spots
Tables A.1 and A.2 show, for each of the 87 hot spot intersections identified by the ranking
methodology, the reference number, its geographical location in easting and northing, the
name of the intersection, and the method by which it was identified, where:
• Agg CR = combined ranking by aggregate measures,
• Range CR = combined ranking by normalized range measures,
• Agg Ind = individual ranking of an aggregate measure, and
• Range Ind = individual ranking of a normalized range measure.
137
Table A.1: List of intersection hot spots - ranking methodology
Reference
Easting
Northing
J1105
J1106
J1523
J1529
J1718
J1726
J1738
J1774
J1787
J1803
J2116
J2147
J2148
J2150
J2157
J2218
J2220
J2225
J2503
J2515
J2518
J2533
J2534
J2551
J2567
J2650
J2657
J2710
J2754
J2764
J2793
J3121
J3130
J3133
J3204
J3229
J3305
J3508
J3514
J3552
J3566
J3576
J3579
J3702
527925
532703
525370
533070
528825
525027
530065
533070
530242
530217
523429
530505
535640
529155
524599
532009
523600
523970
533260
530070
527430
523740
528599
531799
536930
523196
531030
522359
534695
525149
523394
520930
527460
537299
539450
524825
533760
537700
524625
540074
520182
522525
533670
520560
181970
181117
178835
181249
181310
178629
180515
181459
181955
182900
178540
176495
178960
185860
178360
174299
183220
175040
179285
175075
175135
177570
185810
177199
181060
178774
175043
182950
180728
178524
178652
181860
171480
188999
181525
170600
189510
173650
186258
173826
182615
173690
189000
181699
Name
Identification
Baker Street L U Station
Bank L U Station
Earls Court, Cromwell Road
The City,Bishopsgate/Threadneedle Street
Oxford Circus, Cavendish Sq/Holles St
Earl’S Court,West Cromwell Rd/Warwick Rd
Trafalgar Square, Duncannon Street
The City, London Wall/Old Broad Street
Russell Square, South Side
Euston Road / Pancras Road
Hammersmith L U/Bus Station
Stockwell L U Station
Surrey Quays L U Station
Tufnell Park L U Station
West Kensington L U Station
Herne Hill B R Station
Kensal Rise B R Station
Putney B R Station
Bermondsey Square
Clapham Park Road/Acre Lane
Clapham Junction, Northcote Road
Fulham, Lillie Road
Gospel Oak, Highgate Rd/Gordon House Rd
Kennington, Vassall Rd/Camberwell New Rd
Limehouse, Burdett Rd/East India Dock Rd
Hammersmith, Hammersmith Grove
Brixton, Effra Road/St. Matthews Road
College Park, Scrubs Lane/Harrow Road
Shadwell, The Highway/Cannon Street Road
Earl’S Court, Warwick Road/Nevern Place
Hammersmith Broadway/Shepherd’S Bush Roa
North Acton L U Station
Tooting Broadway L U Station
Walthamstow Central L U/B R Station
Canning Town Roundabout
Wimbledon B R Station / Tramlink Stop
Tottenham Swan
Catford, Lewisham Town Hall
Cricklewood Lane/Hendon Way
Lee, St Mildreds Road
Central Middlesex Hospital
Roehampton, Alton Road
Seven Sisters Station
Acton, Gypsy Corner
138
Range CR
Agg CR
Agg CR
Agg CR
Agg Ind
Agg CR
Range CR
Agg CR
Agg Ind
Range CR
Agg Ind
Range CR
Range CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Range CR
Range Ind
Agg Ind
Agg CR
Agg Ind
Agg CR
Agg CR
Range Ind
Range CR
Range Ind
Range CR
Range CR
Range CR
Agg CR
Agg CR
Range Ind
Range CR
Range CR
Table A.2: List of intersection hot spots - ranking methodology (cont.)
Reference
Easting
Northing
J3709
J3718
J3730
J3734
J3755
J3828
J3829
J4529
J4572
J4582
J4713
J4721
J4728
J4739
J4759
J4815
J4832
J4909
J5104
J5110
J5130
J5407
J5423
J5513
J5537
J5791
J6110
J6301
J6515
J6544
J6566
J6571
J6618
J7221
J7242
J7301
J7404
J7426
J7648
J7649
J7736
J7834
J7852
539475
524067
525080
524799
531780
529851
539222
534380
551429
536320
546599
537350
549099
530159
533420
532782
551567
543845
511100
509690
526430
527595
512825
513610
522650
515232
525795
514735
510779
528459
523244
525100
526580
540620
543715
549662
537450
543249
540603
540317
542695
549357
543515
178240
185934
186050
189400
171000
171503
186275
197480
189069
190777
184200
192770
183600
192119
193552
196552
188842
186321
187700
175370
193920
192055
180400
180920
188950
188114
170062
172075
177469
169220
169050
167400
170177
172169
178820
175575
169600
177950
172273
168557
174420
175300
178560
Name
Identification
Blackwall Lane/Woolwich Road
Cricklewood, Westbere Road/Lichfield Rd
Finchley Road, Hendon Way
Temple Fortune, Henlys Corner
Norwood, Crown Point
Streatham, Ambleside Avenue
Leytonstone, Harrow Green
Enfield, Carterhatch La/Gt. Cambridge Rd
Romford, Market Place
Higham Hill, Billet Road/Millfield Ave.
Becontree, Lodge Avenue/Woodward Road
Chingford Mount, New Road/Hall Lane
Dagenham, Chequers Lane/New Road
Bowes Park, Bowes Road/Brownlow Road
Edmonton, Haselbury Road/Northern Avenue
Enfield, Church Street/Sydney Hill
Romford, Mercury Gdns/Western Rd
Ilford, Clements Road
Eastcote L U Station
Hatton Cross L U/Bus Station
Whetstone High Road/Totteridge Lane
Friern Barnet Town Hall
Southall Broadway
Dormers Wells Lane, Telford Road
Hendon, The Burroughs
Harrow, College Road/Kymberley Road
South Wimbledon L U Station
Fulwell, Sixth Cross Road/Wellington Rd.
Cranford, Queen’S Head
Mitcham, Eastfields Level Crossing
Raynes Park, Grand Drive
St. Helier, Central Road
Merton High Street, Merton Bus Garage
Grove Park B R Station
Woolwich Arsenal B R/D L R Station
Bexleyheath Bus Garage, Erith Road
Beckenham Church
Woolwich Common, Nightingale Place
Grove Park, Baring Road/Bus Station
Bromley, Westmoreland Road/Simpsons Road
Eltham Church, High Street/Court Road
Bexleyheath, Arnsberg Way/Mayplace Road
Woolwich, Woolwich New Rd/Sandy Hill Rd
139
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Agg CR
Range Ind
Agg Ind, Range Ind
Range CR
Agg CR
Range CR
Range Ind
Agg Ind
Agg CR
Range Ind
Range CR
Agg CR
Range Ind
Agg CR
Agg CR
Agg CR
Range Ind
Range CR
Range Ind
Agg CR
Agg Ind, Range Ind
Range CR
Agg CR
Agg CR
Range CR
Agg CR
Range CR
Range CR
Agg CR
Agg CR
Range CR
Agg CR
Range CR
Agg CR
Range CR
Agg Ind
Appendix B
Model Descriptive Statistics
Table B.1: Descriptive statistics of variables at the direction level
Run Length (km)
Intersections
Accidents
countRTVHotSpot
countDIHotSpot
countSpeedHotSpot
TotalDelay (min)
Boardings/Trip
MedianTT (min)
AverageTT (min)
VarianceTT (min2)
CongestionIndex
countRTVHotSpot2
countDIHotSpot2
countSpeedHotSpot2
LightBusLane (m)
HeavyBusLane (m)
Mean
St. Dev
Minimum
Maximum
13.30
22.83
28.81
5.59
4.62
7.81
14.84
79.13
47.40
45.90
3.90
2.66
5.77
5.93
5.95
89.68
3341.63
3.46
6.69
17.96
3.28
2.48
4.38
5.50
33.33
12.43
11.54
2.86
0.28
2.90
2.82
2.58
238.90
2265.05
3.8
4.0
2.0
0.0
0.0
1.0
0.9
9.3
13.2
12.5
0.6
1.9
0.0
0.0
0.0
0.0
0.0
24.8
46.0
88.0
22.0
14.0
22.0
38.7
312.9
80.4
77.9
23.1
3.9
13.0
19.0
16.0
2454.4
11449.6
140
Table B.2: Descriptive statistics of variables at the route level
RouteLength (km)
Intersections
Accidents
countRTVHotSpot
countDIHotSpot
countSpeedHotSpot
TotalDelay (min)
MedianTT (min)
AverageTT (min)
VarTT (min2)
FreeFlowTime
CongestionIndex
BusLaneLength (m)
PercentBusLane
Mean
St. Dev
Minimum
Maximum
26.0
44.9
34.0
11.8
9.2
15.9
28.8
88.8
85.7
6.6
32.3
2.7
5505.1
0.2
6.2
13.0
19.8
6.7
5.0
8.5
9.9
20.1
19.9
3.5
7.2
0.2
3132.9
0.1
8.0
8.0
3.0
0.0
0.0
2.0
3.8
26.1
26.1
1.8
10.3
2.0
0.0
0.0
39.3
84.0
93.0
44.0
26.0
39.0
68.0
129.1
127.4
25.0
48.4
3.8
11523.8
0.5
141
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