Supporting Data and Software for the paper: An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
Datacite citation style:
Geržinič, Nejc; Oded Cats; van Oort, Niels; Hoogendoorn-Lanser, Sascha; Hoogendoorn, S.P.(Serge) et. al. (2023): Supporting Data and Software for the paper: An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/45cae66c-7eb3-4e04-85a9-59f6e26cfbb9.v1
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.
For more information, the pre-print of the paper is available on: https://arxiv.org/abs/2301.04982
Information on the data and model can be found in the README file and the python script below.
history
- 2023-03-21 first online, 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 Lab
DATA
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