Developing railway station choice models to improve rail industry demand models

Abstract

This paper describes the development of railway station choice models suitable for defining probabilistic station catchments for use in 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. Results of 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 performance.

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Identifiers

ORCID for Marcus Young: orcid.org/0000-0003-4627-1116

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