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Modelling railway station choice: can probabilistic catchments improve demand forecasts for new stations?

Modelling railway station choice: can probabilistic catchments improve demand forecasts for new stations?
Modelling railway station choice: can probabilistic catchments improve demand forecasts for new stations?
The aim of this thesis is to determine whether the performance of the aggregate rail demand models that are commonly used to forecast demand for new railway stations can be improved by incorporating probabilistic station catchments derived by means of station choice models. The current approaches to forecasting demand for new railway stations have been examined and their limitations identified, and previous work to develop station choice models and incorporate them into demand models has been reviewed. A series of station choice models able to predict station choice at small-scale origin zones were calibrated using revealed preference data from passenger surveys carried out in Scotland and Wales. An automated data processing framework, incorporating a bespoke multi-modal route planner, was developed to derive the model predictor variables from disparate sources of open transport data. The station choice models were found to perform substantially better at predicting station choice than a base model where the nearest station was assumed to be chosen. Trip end models were calibrated for Category E and F stations in Great Britain, using both deterministic and probabilistic station catchments, and a methodology was developed to apply these models to predict demand for new stations and to assess the effect of abstraction on existing stations. The methodology was used to forecast demand at several recently opened stations, including a newly opened line. The models with probabilistic catchments were found to perform better than those with traditional deterministic catchments, and to produce more accurate forecasts than those made during the scheme appraisal process. This is the first known example of successfully incorporating probabilistic station catchments into an aggregate rail demand model, and represents a significant advance over previous work in this area. These findings have important policy implications. They can be used to update industry guidance on best-practice for implementing this type of model in a local context and, more importantly, provide the basis of a robust and transferable national trip end model for Great Britain.
railway station choice, passenger demand forecasting, Discrete choice, Railways
University of Southampton
Young, Marcus
b7679822-1e61-47d0-b7bf-3e33a12fa8fe
Young, Marcus
b7679822-1e61-47d0-b7bf-3e33a12fa8fe
Blainey, Simon
ee6198e5-1f89-4f9b-be8e-52cc10e8b3bb

Young, Marcus (2019) Modelling railway station choice: can probabilistic catchments improve demand forecasts for new stations? University of Southampton, Doctoral Thesis, 340pp.

Record type: Thesis (Doctoral)

Abstract

The aim of this thesis is to determine whether the performance of the aggregate rail demand models that are commonly used to forecast demand for new railway stations can be improved by incorporating probabilistic station catchments derived by means of station choice models. The current approaches to forecasting demand for new railway stations have been examined and their limitations identified, and previous work to develop station choice models and incorporate them into demand models has been reviewed. A series of station choice models able to predict station choice at small-scale origin zones were calibrated using revealed preference data from passenger surveys carried out in Scotland and Wales. An automated data processing framework, incorporating a bespoke multi-modal route planner, was developed to derive the model predictor variables from disparate sources of open transport data. The station choice models were found to perform substantially better at predicting station choice than a base model where the nearest station was assumed to be chosen. Trip end models were calibrated for Category E and F stations in Great Britain, using both deterministic and probabilistic station catchments, and a methodology was developed to apply these models to predict demand for new stations and to assess the effect of abstraction on existing stations. The methodology was used to forecast demand at several recently opened stations, including a newly opened line. The models with probabilistic catchments were found to perform better than those with traditional deterministic catchments, and to produce more accurate forecasts than those made during the scheme appraisal process. This is the first known example of successfully incorporating probabilistic station catchments into an aggregate rail demand model, and represents a significant advance over previous work in this area. These findings have important policy implications. They can be used to update industry guidance on best-practice for implementing this type of model in a local context and, more importantly, provide the basis of a robust and transferable national trip end model for Great Britain.

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More information

Published date: February 2019
Keywords: railway station choice, passenger demand forecasting, Discrete choice, Railways

Identifiers

Local EPrints ID: 430041
URI: http://eprints.soton.ac.uk/id/eprint/430041
PURE UUID: 645c6793-cf47-4867-95d6-d429cc6e25a0
ORCID for Marcus Young: ORCID iD orcid.org/0000-0003-4627-1116
ORCID for Simon Blainey: ORCID iD orcid.org/0000-0003-4249-8110

Catalogue record

Date deposited: 10 Apr 2019 16:30
Last modified: 22 Nov 2021 03:22

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Contributors

Author: Marcus Young ORCID iD
Thesis advisor: Simon Blainey ORCID iD

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