Supporting Data and Software for the paper: An instance-based learning approach for evaluating the perception of ride-hailing waiting time variability

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.<br>For more information, the pre-print of the paper is available on: <em>https://arxiv.org/abs/2301.04982</em><br>Information on the data and model can be found in the README file and the python script below.

下述文件均属于CriticalMaaS研究项目中针对网约车（ride-hailing）与按需出行服务（on-demand transport services）的研究组成部分。本研究聚焦于乘客对等待时长波动性的感知分析，调研向受访者展示了32种假想情境，并针对其选定的备选方案表现提供即时反馈，该反馈信息将被纳入受访者针对后续情境的决策过程中。
如需获取更多研究细节，该论文的预印本可通过以下链接获取：<em>https://arxiv.org/abs/2301.04982</em>
关于本数据集与模型的详细说明，可参阅下述README文件及Python脚本。