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Articles

Railway station choice modelling: a review of methods and evidence

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Pages 232-251 | Received 21 Jan 2016, Accepted 29 Apr 2017, Published online: 14 May 2017
 

ABSTRACT

Since the first railway station choice studies of the 1970s, a substantial body of research on the topic has been completed, primarily in North America, the U.K. and the Netherlands. With many countries seeing sustained growth in rail passenger numbers, which is forecast to continue, station choice models have an important role to play in assessing proposals for new stations or service changes. This paper reviews the modelling approaches adopted, the factors found to influence station choice and the application of models to real-world demand forecasting scenarios. A consensus has formed around using the closed-form multinomial logit and nested logit models, with limited use of more advanced simulation-based models, and the direction effects of a range of factors have been consistently reported. However, there are questions over the validity of applying non-spatial discrete choice models to a context where spatial correlation will be present, in particular with regard to the models’ ability to adequately represent the abstraction behaviours resulting from competition between stations. Furthermore, there has been limited progress towards developing a methodology to integrate a station choice element into the aggregate models typically used to forecast passenger demand for new stations.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Assuming a two-level NL.

2. The IV parameter represents the degree to which unobserved factors are correlated and alternatives in a nest are substitutes for one another.

3. A “flow model” is a spatial interaction model that estimates passenger flows between all origin-destination station pairs based on attributes of the origin, destination, and their separation.

4. The unit postcode typically represents around 15 addresses.

Additional information

Funding

This work was supported by Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/M50662X/1].

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