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

Dataset

x choose version: version 2 - 2023-03-23 (latest)

• 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.

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.

• 2023-03-21 first online, 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