[Supporting Data and Software] 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>More information about the research and the data can be found in the files below and the linked pre-print.

以下收录的文件均属于针对网约车（ride-hailing）与按需出行服务（on-demand transport services）开展的CriticalMaaS研究的组成部分。本研究针对乘客对等待时间波动性的感知展开分析：研究人员向受访者展示了32种假想情境，并针对其选定的备选出行方案的表现提供即时反馈；该反馈信息随后将被纳入受访者针对下一情境的决策考量。
有关本研究及数据集的更多详情，可参阅下方文件及所关联的预印本（pre-print）。