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
Delft University of Technology logo
usage stats
995
views
573
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.


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
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)