What is the market potential for on-demand services as a train station access mode?

Abstract

On-demand mobility services (FLEX) are often proposed as a solution for the first/last mile problem. We study the potential of using FLEX to improve train station access by means of a three-step sequential stated preference survey. We compare FLEX with the bicycle, car and public transport for accessing two alternative train stations. We estimate a joint access mode and train station choice model. Estimating a latent class choice model with different nesting structures, we uncover four distinct segments in the population. Two segments (∼50%) with a lower Willingness-to-Pay seem to be more likely to take-up FLEX. Ex-urban car drivers seem to be the most likely segment to adopt FLEX, showing great, since members of this segment are currently frequent users of the private car. Our case study also shows that while FLEX competes primarily with public transport when accessing local stations, it competes primarily with car for reaching distant stations.

KEYWORDS:

• Access modes
• train station choice
• on-demand mobility
• stated choice
• latent class

Acknowledgements

The authors thank The Netherlands Institute for Transport Policy Analysis (KiM) for facilitating the survey data collection and the Dutch Mobility Panel (MPN) participants for their time and effort in responding to the survey. The authors confirm contribution to the paper as follows: Study conception and design: Geržinič, Cats, van Oort; Survey design and data gathering: Geržinič, Hoogendoorn-Lanser, van Oort, Cats; Analysis and interpretation of results: Geržinič, van Oort, Cats; Draft manuscript preparation: Geržinič, van Oort, Cats; Supervision and reviewing: Cats, van Oort, Hoogendoorn; Funding acquisition: Cats. All authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the CriticalMaaS project [grant number 804469], which is financed by the European Research Council and Amsterdam Institute for Advanced Metropolitan Solutions.