[Supporting Data and Software] An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
The doi 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
Datacite citation style:
Geržinič, Nejc; Oded Cats; van Oort, Niels; Hoogendoorn-Lanser, Sascha; Hoogendoorn, S.P.(Serge) et. al. (2023): [Supporting Data and Software] An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/45cae66c-7eb3-4e04-85a9-59f6e26cfbb9.v2Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
version 2 - 2023-03-23 (latest)version 1 - 2023-03-21
licenceCC BY-NC 4.0
The files included below are part of the CriticalMaaS research on ride-hailing and on-demand transport services. In this study, passengers' perception of waiting time variability was analysed.
Respondents were presented with 32 hypothetical scenarios with immediate feedback on the performance of their selected alternatives. This feedback information was then incorporated into their decision-making for the following scenario.
More information about the research and the data can be found in the files below and the linked pre-print.
- 2023-03-21 first online
- 2023-03-23 published, posted
associated peer-reviewed publicationAn instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
- CriticalMaaS (grant code 804469) European Research Council
organizationsTU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Smart Public Transport Lab
- download all files (zip)
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