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

Version 2 - 2023-03-23 (latest)

Usage statistics

Categories

• Ground Transport
• Transport

Keywords

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

Geolocation

Time coverage

2021

Licence

CC BY-NC 4.0

Interoperability

• RO-Crate Metadata

Export as...

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.

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