%0 Generic
%A Geržinič, Nejc
%A Cats, Oded
%A van Oort, Niels
%A Hoogendoorn-Lanser, Sascha
%A Hoogendoorn, S.P.(Serge)
%A Bierlaire, Michel
%D 2023
%T Supporting Data and Software for the paper: An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability
%U 
%R 10.4121/45cae66c-7eb3-4e04-85a9-59f6e26cfbb9.v1
%K Instance-based learning
%K Memory decay
%K Choice modelling
%K Ride-hailing
%K Waiting time
%K Service reliability
%X <p>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.</p><p>&nbsp;</p><p>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.</p><p><br></p><p>For more information, the pre-print of the paper is available on: <em>https://arxiv.org/abs/2301.04982</em></p><p><br></p><p>Information on the data and model can be found in the README file and the python script below.</p>
%I 4TU.ResearchData