Development of railway station choice models to improve the representation of station catchments in rail demand models
Development of railway station choice models to improve the representation of station catchments in rail demand models
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.
railway station choice; discrete choice models; passenger demand forecasting
80-103
Young, Marcus
b7679822-1e61-47d0-b7bf-3e33a12fa8fe
Blainey, Simon P.
ee6198e5-1f89-4f9b-be8e-52cc10e8b3bb
2018
Young, Marcus
b7679822-1e61-47d0-b7bf-3e33a12fa8fe
Blainey, Simon P.
ee6198e5-1f89-4f9b-be8e-52cc10e8b3bb
Young, Marcus and Blainey, Simon P.
(2018)
Development of railway station choice models to improve the representation of station catchments in rail demand models.
Transportation Planning and Technology, 41 (1), .
(doi:10.1080/03081060.2018.1403745).
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.
Text
Development of railway station choice models to improve the representation of station catchments in rail demand models
- Accepted Manuscript
More information
Accepted/In Press date: 14 September 2017
e-pub ahead of print date: 23 November 2017
Published date: 2018
Keywords:
railway station choice; discrete choice models; passenger demand forecasting
Identifiers
Local EPrints ID: 415843
URI: http://eprints.soton.ac.uk/id/eprint/415843
ISSN: 1029-0354
PURE UUID: 5ed28026-f09b-4b19-a221-874208eff894
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Date deposited: 24 Nov 2017 17:31
Last modified: 16 Mar 2024 05:47
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