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Development of railway station choice models to improve the representation of station catchments in rail demand models

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  • Marcus A. Young
  • Simon P. Blainey

Abstract

This paper describes the development of railway station choice models suitable for defining probabilistic station catchments. These catchments can then be incorporated into the aggregate demand models typically used to forecast demand for new rail stations. Revealed preference passenger survey data obtained from the Welsh and Scottish Governments was used for model calibration. Techniques were developed to identify trip origins and destinations from incomplete address information and to automatically validate reported trips. A bespoke trip planner was used to derive mode-specific station access variables and train leg measures. The results from a number of multinomial logit and random parameter (mixed) logit models are presented and their predictive performance assessed. The models were found to have substantially superior predictive accuracy compared to the base model (which assumes the nearest station has a probability of one), indicating that their incorporation into passenger demand forecasting methods has the potential to significantly improve model predictive performance.

Suggested Citation

  • Marcus A. Young & Simon P. Blainey, 2018. "Development of railway station choice models to improve the representation of station catchments in rail demand models," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(1), pages 80-103, January.
    • Handle: RePEc:taf:transp:v:41:y:2018:i:1:p:80-103
    DOI: 10.1080/03081060.2018.1403745
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    References listed on IDEAS

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    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    4. Sener, Ipek N. & Pendyala, Ram M. & Bhat, Chandra R., 2011. "Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior," Journal of Transport Geography, Elsevier, vol. 19(2), pages 294-303.
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