[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
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
usage stats
995
views
573
downloads
categories
geolocation
Netherlands
time coverage
2021
licence
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
associated peer-reviewed publication
An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
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 LabDATA
files (5)
- 1,477 bytesMD5:
346dc527a32a3c57d841752aab53d29d
README.txt - 8,585 bytesMD5:
a62b6df7639f7cace7cf6847ef785d08
choice_model.py - 10,189 bytesMD5:
aa29d26ad2df3b169fd863bc0adfadd2
example_dataset.csv - 21,133 bytesMD5:
977ecf83de2dced2101c296fbd2ac85a
model_outcome.html - 115,037 bytesMD5:
fd9d395e1321478c4ad1265c5e793243
survey_transcript.docx -
download all files (zip)
156,421 bytes unzipped