An exploratory analysis of rail travel
time and fare differences between
London and the North using publicly
available datasets.
The Institute For Transport Studies – The University of Leeds
Dr Andrew Mark Tomlinson
23rd April 2015
Class 87 at Crewe, April 1977
3 x Class 86’s at Preston,
April 1977
Class 08 shunter waiting for
work at Preston, April 1977
Presentation Aims
• To introduce and raise awareness of two
useful rail datasets
• To outline the content of the datasets and
the difficulties associated with using them
• To demonstrate the use of the datasets in
an example problem
• To report on leading edge research
Station Usage Data
• Shows Passenger Entries/Exits/Interchanges
• Differentiates between Peak, Off-Peak and Seasons
• 1997 onwards
• http://orr.gov.uk/statistics/published-stats/station-usage-estimates
• Excel format, with notes on methodology
• Better estimate of total
passenger trips
compared to ORR
headline figure
(1332.5M vs 1600M)
Leeds Station: Total Entries 1998 - 2014
UK Centres of
Gravity
(using rail station
usage data)
• UK Population
• Rail Station
• Rail Passengers
Median Method:
Number North=Number South &
Number West=Number East
UK Population:
Centre of Gravity
(Polesworth)
UK Rail Station:
Centre of Gravity
(Olton)
UK Rail Passengers:
Centre of Gravity
(London Marylebone)
UK Rail Passengers:
Centre of Gravity
(London Paddington)
How does the daily commute differ
between London and the North?
• Fare Paid
• Journey Time
Examined using publicly
available datasets:
• ATOC Timetable Data
• ATOC Fares Data
Timetable Dataset
• UK timetable available in electronic form from ATOC
(http://data.atoc.org/how-to)
– Text based fixed format files defined according to CIF End User Specification
(www.atoc.org/clientfiles/files/RSPDocuments/20070801.pdf)
• Stations (nodes)
– identified by CRS (3-letter) code and TIPLOC (timing point location)
– geocoded to within 500m using Easting/Northing pair
• Services (links)
– Header record:
• validity, days of operation, head-code, power-type, speed, class, TOC
– Details records (one per pair of adjacent stations):
• Arrival/departure times, allowances, special instructions/activities
• Problems
– Missing interchange times for large stations?
• Stations + Service records create a 3 dimensional network (x, y, and
time).
• Traversing this network yields all routes and timings between two points
Timetable Dataset Example
Service Header
Station
Arrive
Depart
Service UID
Y52133
HUD
From
14/12/2014
SWT
10:22
10:22
To
10/05/2015
MSN
10:27
10:28
Days Run
0000001
GFD
10:36
10:36
Head-code
2M63
MSL
10:41
10:41
Power Type
DMU
SWT
10:45
10:46
Speed
075
AHN
10:50
10:50
Timing Load
A
MCV
11:04
Train Class
S
TOC (X Header)
NT
10:15
• 2,953 station records,
• 70,166 train service headers (period December 2014 – May 2015)
• 837,007 train service movements (between pairs of stations)
Fares Dataset
• All UK rail fares available in electronic form from ATOC
– Text based fixed format files
– Uses a mix of CRS and NLC codes to identify locations
– Comprehensive description of each table and field available
(http://data.atoc.org/sites/all/themes/atoc/files/SP0035.pdf)
– Split into standard fares and non-derivable, TOC specific and Advance
purchase fares
– Useful other information: restrictions, discounts, rounding, rail cards,
rovers, supplements
• Problems
– Dataset very large
• standard fares alone can be imported into Access
• Importing other fares cause Access 2GB limit to be exceeded
– No information about how to query the data
• Reverse engineering + Validation
• Standalone Advantix Traveller application also available (much
faster than the web)
Finding a Fare
Station
Cluster
One-way fare
Station
Cluster
Two-way fare
Destination
Origin
CRS: HUD
NLC: 8437
+ Group Stations (Bradford Stations)
+ Ticket Type: return/single, anytime/off-peak, first/standard
+ Route + Restrictions: Via / Not Via, Valid / Not Valid
CRS: LDS
NLC: 8487
Avantix Standalone Application
Four Northern Cities
Liverpool:
MPTE - LIV
Manchester:
GMPTE - MAN
London: TfL LON
Leeds:
WYPTE LDS
Sheffield:
SYPTE SHF
Model Specification(s)
Attribute
Value
Model Type
Linear (OLS)
2 x Models
1. Destination LDS + MAN + SHF + LIV
2. Destination LON
2 x Dependant
variables
A. One way fare to centre, £ (Anytime day return/2)
B. Travel Time to centre, minutes (including waiting time)
Independent
variables
Variable A (Fare)
Model 1
• Cartesian Distance
(km)
• Is Not in PTE
(Dummy)
• Is City X (dummy)
Variable B (Time)
Model 2
• Cartesian
Distance (km)
Models 1 + 2
• Cartesian Distance
(km)
• Is Not Direct
(Dummy)
Filter
Origin >5 km, Not HS1 station, Journey Time < 90 minutes, Day Return
Data Points
LON: 427, LDS: 119, LIV: 177, MAN: 228, SHF: 111
Results Model A (Fare)
Model 1 (North)
Model 2 (London)
n
635
427
Adjusted R2
0.80
0.84
Standard Error
1.04
1.32
B
Std. Err
B
Std. Err
Constant: Access Charge (£)
1.01
0.105
1.00
0.137
Distance (£/km)
0.12**
0.005
0.25**
0.005
Not in PTE (£)
1.37**
0.126
Is MAN (£)
0.44**
0.087
• Fares increase (almost) linearly with distance
• Access charge becomes less significant as distance increases
• Fares within the ‘home’ PTE region cheaper than those outside PTE
region
Results Model B (Travel Time)
•
•
•
•
Model 1 (North)
Model 2 (London)
n
635
427
Adjusted R2
0.69
0.55 !
Standard Error
10.7
8.5
B
Std. Err
B
Std. Err
Constant (minutes)
8.45
1.02
11.82
0.88
Distance (minutes/km)
1.11**
0.04
0.78**
0.03
Change needed (minutes) 6.73**
0.98
1.56
0.96
Fit not that good
Travel time increases (approximately) linearly with distance
Overall journey times are shorter in London
Impact of changes more significant in North
What proportion of fares difference
can be attributed to time savings?
Model 1 (North)
Model 2 (London)
n
635
427
Adjusted R2
0.79
0.84
Standard Error
2.14
1.82
B
Std. Err
B
Std. Err
Constant (£)
2.14
0.217
2.47
0.188
Distance (£/km)
0.26**
0.01
0.35**
0.007
Not in PTE (£)
2.28**
0.26
• Model rephrased to include Value of Journey time @ £6.81/hour (commuting
VOT, 2014)
• Difference suggests that time saving benefits represent 25%-30% of fare
premium paid by Londoners
• Some value could also be attached to other quality attributes (The Hated
Pacers!)
London vs North
Attribute
Winner
Notes
Journey Times
London (≈ 20km/h
faster)
Effect of Changing
Trains
London (fewer
and less
disruptive)
Fares
North (≈ £0.12/km VOT benefits account for 25% of difference
cheaper)
Fare Boundaries
London
(fewer/none)
PTE boundaries create artificial barriers,
impose financial penalty on cross boundary
travel (compare with VRR in Germany)
Day Return Tickets
London (available
from all origins)
London: 427 (99.8%) out of 428
North: 635 (91.2%) out of 696
Thirsk-Leeds, Preston-Manchester
Longer Distance
Commuting
London
Limited opportunities for commuting from
>50km in North
London: 103 (24%) average wait 7.2 minutes
North: 238 (38%) average wait 12.6 minutes
Further Uses
• To create a repository of all timetables and fares data
going forward
• To study evolving service patterns in order to write a
narrative around the changing nature of passenger
rail travel/industry
• Combine:
– fares, timetable and station entry/exit data
– population and employment data
To reverse engineer/synthesise a public OD trip matrix
How does the cost of car commuting
compare to rail ?
AA (July 2014), ‘Average’ Petrol car
• Fixed costs £3,678
• Running costs £0.13/km (@ £1.09/litre)
• Commuting assumed 5 days/week for 46 weeks/year
• Excludes parking costs and values of difference in journey time
How does the cost of car commuting
compare to rail ?
•
•
•
Rail cheaper than car when fixed costs are included
Discounts on Season tickets would make fares almost equivalent to running costs only
Assumes Single Occupancy Vehicle (SOV)