[Supporting Data and Software] An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

doi: 10.4121/45cae66c-7eb3-4e04-85a9-59f6e26cfbb9.v2
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
doi: 10.4121/45cae66c-7eb3-4e04-85a9-59f6e26cfbb9
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.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
choose version:
version 2 - 2023-03-23 (latest)
version 1 - 2023-03-21
Delft University of Technology logo
usage stats
913
views
508
downloads
geolocation
Netherlands
time coverage
2021
licence
cc-by-nc.png logo CC 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.

history
  • 2023-03-21 first online
  • 2023-03-23 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)