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Modelling station choice

Modelling station choice

My first presentation as a PhD student in which I outline the background to my research project. This presentation was given as part of the University of Southampton Transportation Research Group seminar programme.

Marcus Young

April 10, 2015
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  1. Contents  Demand models for new stations  Defining station

    catchments  Catchments in reality  Probabilistic station choice – discrete choice models  Next steps
  2. 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 i V f population  ( , , , ) i i i i i V f population frequency parking jobs 
  3. 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 ij T f O D S 
  4. 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 2 i i i V Pop Pop       i V Pop    
  5. Defining station catchments  Nearest station – zone based: 

    Choice of station is deterministic.  Catchments are discrete, non overlapping.
  6. 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).
  7. 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
  8. 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.
  9. 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  
  10. Discrete choice models Station Access Distance (km) Direct destinations Off-

    peak fare to London (£) Journey time 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
  11. 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.
  12. Logit models  Binary logit (choice of two alternatives, i

    and j):  Multinomial logit (e.g. three alternatives, i,j and k): Pr( ) ni nj ni MeasuredUtility MeasuredUtility MeasuredUtility e ni e e   Pr( ) ni nj ni nk MeasuredUtility MeasuredUtility MeasuredUtility MeasuredUtility e ni e e e   
  13. 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.
  14. 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? ( ) 0.4 2 ( ) 0.2 P A P C   ( ) 0.66 2 ( ) 0.33 P A P C  
  15. 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.
  16. 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 Digimap Ordnance Survey Service, <http://edina.ac.uk/digimap>, Downloaded: April 2015. 250K Raster [TIFF geospatial data], Ordnance Survey (GB), Using: EDINA Digimap Ordnance Survey Service, <http://edina.ac.uk/digimap>, Downloaded: April 2015.