Modelling station choiceMarcus Young

University of Southampton

10 April 2015

Contents

� Demand models for new stations

� Defining station catchments

� Catchments in reality

� Probabilistic station choice – discrete choice models

� Next steps

Simple demand models

Used to forecast the number of entries and exits (Vi) at a new station:

� Trip rate model - function of population of catchment:

� Trip end model - function of population plus other factors:

( )i iV f population=

( , , , )i i i i iV f population frequency parking jobs=

Spatial interaction (flow) models

Used to forecast the number of trips (T) from each origin (i) station to each

destination (j) station:

� Oi – attributes of origin (e.g. population, parking, frequency)

� Dj – attributes of destination (e.g. number of workplaces)

� Sij – separation between origin and destination (e.g. journey time)

( )ij i j ijT f O D S=

Defining station catchments

� Calibrate models using observed entries/exits or flows at existing stations.

� But must define a catchment first.

� Circular (buffer) around station:

1 2i i iV Pop Popα β γ= + +iV Popα β= +

Defining station catchments

� Nearest station – zone based:

� Choice of station is deterministic.

� Catchments are discrete, non overlapping.

Catchments in reality

� Use origin-destination surveys.

� 2km circular catchments account on average for 57%

of observed trips – between 0-20% for some stations

(Blainey and Evens, 2011).

� Only 53% of trip ends located within zone-based

catchments (Blainey and Preston, 2010).

� 47% of passengers in the Netherlands do not use their

nearest station (Debrezion et al., 2007).

Catchments in reality

� Catchments are not discrete, they overlap,

and stations compete.

� Station choice is not homogenous within

zones.

� Catchments vary by access mode and station

type.

� Station choice more complex than models

allow – need an alternative.

Mahmoud et al., 2014

Improving demand forecasting models

� Include a probability-based station choice

element.

� Should produce more accurate and

transferable models.

� For each catchment zone calculate the

probability of each competing station being

chosen.

� Allocate zonal population to each station based

on the probabilities.

Discrete choice models

� Individual chooses from a finite

number of mutually exclusive

alternatives.

� Individual chooses the alternative

that maximises their utility

(satisfaction).

Factor Change Expected affect on utility

Frequency of

service� �

Car parking spaces � �

Fare � �

Access distance � �

Interchanges � �

Journey time � �

Discrete choice models

Station Access Distance (km)

Direct destinations

Off-peak fare to London (£)

Journeytime to London (mins)

Transfers (to London)

Frequency per day (to London)

Parking Spaces

Pen Mill 0.5 Cardiff-

Weymouth

86.00 206 1 8 25

Yeovil Junction

2.1 Waterloo-

Exeter

52.00 140 0 19 199

Castle Cary

24.1 Paddington-

Penzance

86.00 100 0 8 120

Discrete choice models

� Actual utility an individual gains from an alternative is not

known.

� Researcher tries to measure utility by identifying

attributes of the alternatives and/or the individual:

Utility = Measured utility + Unobserved utility

Measured utility = αFreq + βFare + γPkg + δDis

� If we assume that the unobserved utility of the

alternatives is independent of each other and identically

distributed (extreme value) then can use logit models.

Logit models

� Binary logit (choice of two alternatives, i and j):

� Multinomial logit (e.g. three alternatives, i,j and k):

Pr( )ni

njni

MeasuredUtility

MeasuredUtilityMeasuredUtility

eni

e e=

+

Pr( )ni

njni nk

MeasuredUtility

MeasuredUtilityMeasuredUtility MeasuredUtility

eni

e e e=

+ +

Estimating logit models

� Need to estimate the parameters in the utility function:

Measured utility = αFreq + βFare + γPkg + δDis

� Collect individual-level data – usually from in-train passenger surveys.

� Dependent variable is the observed choice (the station each participant

actually chose).

� Parameters are estimated using maximum likelihood estimation - R, STATA,

LIMDEP.

Logit models - substitution behaviour

� Independence from irrelevant alternatives (IIA).

� For each pair of alternatives, the ratio of their probabilities is not affected by adding or

removing another alternative, or changing the attributes of another alternative.

� Consequence – proportional substitution pattern.

� Stations are located in space.

� Are a-spatial choice models appropriate?

( ) 0.42

( ) 0.2

P A

P C= =

( ) 0.662

( ) 0.33

P A

P C= =

Next steps

� Obtain and prepare data:

� Transport Scotland ≈ 23,000 responses

� London Travel Demand Survey 2005/06 to 2012/13 –

but rail trips a minor component.

� Carry out on-train survey?

� Big-data: transport timetables

� Descriptive analysis – observed catchments.

� Develop and validate choice models.

� Incorporate choice models into trip-end, flow models.

References

Debrezion, G., Pels, E. and Rietveld, P. (2007) “Choice of Departure Station by Railway Users,” European Transport, 37, 78–92.

Blainey, S. P. and Preston, J. M. (2010) “Modelling Local Rail Demand in South Wales,” Transportation Planning and Technology, 33, 55–73.

Blainey, S. and Evens, S. (2011) “Local Station Catchments: Reconciling Theory with Reality.” In European Transport Conference.

Mahmoud, M. S., Eng, P. and Shalaby, A. (2014) “Park-and-Ride Access Station Choice Model for Cross-Regional Commuter Trips in the Greater Toronto and Hamilton Area (GTHA).” In Transportation Research Board 93rd Annual Meeting.

50K Raster [TIFF geospatial data], Ordnance Survey (GB), Using: EDINA DigimapOrdnance Survey Service, <http://edina.ac.uk/digimap>, Downloaded: April 2015.

250K Raster [TIFF geospatial data], Ordnance Survey (GB), Using: EDINA DigimapOrdnance Survey Service, <http://edina.ac.uk/digimap>, Downloaded: April 2015.