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

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

Dataset

x choose version: version 1 - 2023-03-21

• Ground Transport
• Transport

Choice modelling, Instance-based learning, Memory decay, Ride-hailing, Service reliability, Waiting time

2021

CC BY-NC 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Nejc Geržinič, Oded Cats, Niels van Oort, Sascha Hoogendoorn-Lanser, S.P.(Serge) Hoogendoorn , Michel Bierlaire

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

4TU.ResearchData

*.py, *.html,*.csv,*.docx

An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

• CriticalMaaS (grant code 804469) European Research Council

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Smart Public Transport Lab