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 23 rd 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 Rail Station: Centre of Gravity (Olton) UK Population: Centre of Gravity (Polesworth)

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

Service Header

Service UID From To Days Run Head-code Power Type Speed Timing Load Train Class TOC (X Header)

Timetable Dataset Example

Y52133 14/12/2014 10/05/2015 0000001 2M63 DMU 075 A S NT

Station

HUD SWT MSN GFD MSL SWT AHN MCV

Arrive

10:22 10:27 10:36 10:41 10:45 10:50 11:04

Depart

10:15 10:22 10:28 10:36 10:41 10:46 10:50 • • • 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 Station Cluster One-way fare Two-way fare Destination CRS: LDS NLC: 8487 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

Avantix Standalone Application

Liverpool: MPTE - LIV

Four Northern Cities

Manchester: GMPTE - MAN Leeds: WYPTE LDS London: TfL LON Sheffield: SYPTE SHF

Attribute

Model Type 2 x Models 2 x Dependant variables Independent variables Filter Data Points

Model Specification(s)

Value

Linear (OLS) 1. Destination LDS + MAN + SHF + LIV 2. Destination LON A. One way fare to centre, £ (Anytime day return/2) B. Travel Time to centre, minutes (including waiting time) 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) • Cartesian Distance (km) • • Cartesian Distance (km) Is Not Direct (Dummy) Origin >5 km, Not HS1 station, Journey Time < 90 minutes, Day Return LON: 427, LDS: 119, LIV: 177, MAN: 228, SHF: 111

n Adjusted R2 Standard Error

Results Model A (Fare)

Constant: Access Charge (£) Distance (£/km) Not in PTE (£) Is MAN (£)

Model 1 (North)

635 0.80

1.04

B Std. Err 0.105

1.01

0.12** 1.37** 0.44** 0.005

0.126

0.087

Model 2 (London)

427 0.84

1.32

B 1.00

0.25** Std. Err 0.137

0.005

• • • 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)

n Adjusted R2 Standard Error Constant (minutes) Distance (minutes/km)

Model 1 (North)

635 0.69

10.7

B 8.45

1.11** Change needed (minutes) 6.73** Std. Err 1.02

0.04

0.98

Model 2 (London)

427 0.55 !

8.5

B 11.82

0.78** 1.56

Std. Err 0.88

0.03

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?

n Adjusted R2 Standard Error Constant (£) Distance (£/km) Not in PTE (£)

Model 1 (North)

635 0.79

2.14

B 2.14

0.26** 2.28** Std. Err 0.217

0.01

0.26

Model 2 (London)

427 0.84

1.82

B 2.47

0.35** Std. Err 0.188

0.007

• • • 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!)

Attribute

Journey Times Effect of Changing Trains Fares Fare Boundaries Day Return Tickets Longer Distance Commuting

London vs North

Winner

London (≈ 20km/h faster) London (fewer and less disruptive) North (≈ £0.12/km cheaper) London (fewer/none) London (available from all origins) London

Notes

London: 103 (24%) average wait 7.2 minutes North: 238 (38%) average wait 12.6 minutes VOT benefits account for 25% of difference PTE boundaries create artificial barriers, impose financial penalty on cross boundary travel (compare with VRR in Germany) London: 427 (99.8%) out of 428 North: 635 (91.2%) out of 696 Thirsk-Leeds, Preston-Manchester 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)