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Trip end models of local rail demand in England and Wales

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  • Blainey, Simon

Abstract

This paper details models which have been developed to forecast the total number of trips made from local rail stations in England and Wales over a one year period. The use of multiple linear regression and geographically weighted regression in calibration are compared, with both explaining over 75% of the variation in the observed data. The latter technique has not previously been used in rail demand modelling, and allows significant spatial variations in the effect of independent variables to be identified and mapped. A number of catchment definition methods are investigated, as is the inclusion of a wide range of demographic and service related explanatory variables. The models developed are used to forecast usage at stations on the recently opened Ebbw Vale branch line in South Wales and these predictions are compared to initial usage figures.

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  • Blainey, Simon, 2010. "Trip end models of local rail demand in England and Wales," Journal of Transport Geography, Elsevier, vol. 18(1), pages 153-165.
    • Handle: RePEc:eee:jotrge:v:18:y:2010:i:1:p:153-165
    DOI: 10.1016/j.jtrangeo.2008.11.002
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    1. Wardman, Mark & Lythgoe, William & Whelan, Gerard, 2007. "Rail Passenger Demand Forecasting: Cross-Sectional Models Revisited," Research in Transportation Economics, Elsevier, vol. 20(1), pages 119-152, January.
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    9. Blainey, Simon P. & Preston, John M., 2013. "A GIS-based appraisal framework for new local railway stations and services," Transport Policy, Elsevier, vol. 25(C), pages 41-51.
    10. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
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    12. Caset, Freke & Blainey, Simon & Derudder, Ben & Boussauw, Kobe & Witlox, Frank, 2020. "Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium," Journal of Transport Geography, Elsevier, vol. 87(C).
    13. Pirdavani, Ali & Bellemans, Tom & Brijs, Tom & Kochan, Bruno & Wets, Geert, 2014. "Assessing the road safety impacts of a teleworking policy by means of geographically weighted regression method," Journal of Transport Geography, Elsevier, vol. 39(C), pages 96-110.
    14. Ibeas, Ángel & Cordera, Ruben & dell'Olio, Luigi & Moura, Jose Luis, 2011. "Modelling demand in restricted parking zones," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 485-498, July.
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    17. Yang, Hongtai & Lu, Xiaozhao & Cherry, Christopher & Liu, Xiaohan & Li, Yanlai, 2017. "Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 64(C), pages 184-194.
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    19. Mulley, Corinne & Tsai, Chi-Hong (Patrick) & Ma, Liang, 2018. "Does residential property price benefit from light rail in Sydney?," Research in Transportation Economics, Elsevier, vol. 67(C), pages 3-10.
    20. Wang, Chih-Hao & Chen, Na, 2017. "A geographically weighted regression approach to investigating the spatially varied built-environment effects on community opportunity," Journal of Transport Geography, Elsevier, vol. 62(C), pages 136-147.

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