Understanding the distribution of passenger flow is one of the crucial prerequisites for improving operational efficiency and implementing travel demand management strategies. This paper proposes an improved Random Regret Minimization (i-RRM) model based on the regret theory, where the passenger’s regret on selecting routes is minimized. Considering the growing trend of passengers relying on smartphone apps for route planning and navigation, three factors are investigated, namely, travel time, the number of transfer stations, and the total number of stations traveled. Automatic Fare Collection (AFC) is used to get the proportion of each route, and mobile phone signaling data is used to obtain the ground truth of passengers’ routes proportions for parameter calibration and model validation. The results show that, compared to the ground truth data, the average errors of the proposed i-RRM model, the classic RRM model, and the direct path matching algorithm are 6.03%, 8.96%, and 10.45%, respectively.

中文翻译：

---

了解地铁乘客使用智能手机应用程序的路线选择行为

了解客流分布是提高运营效率和实施出行需求管理策略的重要前提之一。本文提出了一种基于后悔理论的改进随机后悔最小化（i-RRM）模型，其中乘客对选择路线的后悔被最小化。考虑到乘客越来越依赖智能手机应用程序进行路线规划和导航，我们研究了三个因素，即出行时间、换乘站数量和总出行站数。利用自动售检票（AFC）获取各线路比例，并利用手机信令数据获取旅客线路比例的真实情况，进行参数标定和模型验证。结果表明，与地面实况数据相比，所提出的i-RRM模型、经典RRM模型和直接路径匹配算法的平均误差分别为6.03%、8.96%和10.45%。