[Supporting Data and Software] What is the market potential for on-demand services as a train station access mode?

DOI:10.4121/bd9232f4-4336-437f-95c8-3b96e1bdc9ee.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/bd9232f4-4336-437f-95c8-3b96e1bdc9ee

Datacite citation style

Geržinič, Nejc; Oded Cats; van Oort, Niels; Hoogendoorn-Lanser, Sascha; Hoogendoorn, S.P.(Serge) (2023): [Supporting Data and Software] What is the market potential for on-demand services as a train station access mode?. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bd9232f4-4336-437f-95c8-3b96e1bdc9ee.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Delft University of Technology logo

Usage statistics

302
views
1295
downloads

Geolocation

Netherlands

Time coverage

2020

Licence

CC BY-NC 4.0

Interoperability

The files included below are part of the CriticalMaaS research on ride-hailing and on-demand transport services. In this study, passengers' preferences for multi-modal commute trips in the Netherlands were analysed.


A multi-stage mode choice stated preference survey was carried out in the Netherlands in February 2020. Respondents had to indicate their preference for an access mode to two different hypothetical train stations, followed by a choice for one of the two train station, given their preferred access mode and the railway service characteristics at the respective station. The access modes among which respondents could choose were bike, car, public transport or FLEX (on-demand service). In total, six mutli-modal trips were analysed.


More information about the research and the data can be found in the files below and the linked paper.

History

  • 2023-03-23 first online, published, posted

Publisher

4TU.ResearchData

Format

*.py, *.html,*.csv,*.docx

Funding

  • CriticalMaaS (grant code 804469) European Research Council

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Smart Public Transport Lab

DATA

Files (5)