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 LeedsDr 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 Paddington)

UK Rail Passengers: Centre of Gravity

(London Marylebone)

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 ExampleService Header

Service UID Y52133

From 14/12/2014

To 10/05/2015

Days Run 0000001

Head-code 2M63

Power Type DMU

Speed 075

Timing Load A

Train Class S

TOC (X Header) NT

Station Arrive Depart

HUD 10:15

SWT 10:22 10:22

MSN 10:27 10:28

GFD 10:36 10:36

MSL 10:41 10:41

SWT 10:45 10:46

AHN 10:50 10:50

MCV 11:04

• 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

Origin

Destination

CRS: HUDNLC: 8437

CRS: LDSNLC: 8487

One-way fare

Two-way fare

StationCluster

StationCluster

+ Group Stations (Bradford Stations)

+ Ticket Type: return/single, anytime/off-peak, first/standard

+ Route + Restrictions: Via / Not Via, Valid / Not Valid

Avantix Standalone Application

Four Northern Cities

Leeds: WYPTE -LDS

Sheffield: SYPTE -SHF

Manchester: GMPTE - MAN

Liverpool: MPTE - LIV

London: TfL -LON

Model Specification(s)Attribute Value

Model Type Linear (OLS)

2 x Models 1. Destination LDS + MAN + SHF + LIV2. 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) Variable B (Time)

Model 1 Model 2 Models 1 + 2

• Cartesian Distance (km)

• Is Not in PTE (Dummy)

• Is City X (dummy)

• CartesianDistance (km)

• 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)

• 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

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

Results Model B (Travel Time)

• 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

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

What proportion of fares difference can be attributed to time savings?

• 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!)

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

London vs North

Attribute Winner Notes

Journey Times London (≈ 20km/hfaster)

Effect of ChangingTrains

London (fewer and less disruptive)

London: 103 (24%) average wait 7.2 minutesNorth: 238 (38%) average wait 12.6 minutes

Fares North (≈ £0.12/kmcheaper)

VOT benefits account for 25% of difference

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 (availablefrom all origins)

London: 427 (99.8%) out of 428North: 635 (91.2%) out of 696

Thirsk-Leeds, Preston-Manchester

Longer Distance Commuting

London Limited opportunities for commuting from >50km in North

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)