Smart card data from the Automatic Fare Collecting systems (AFC) and timetable information, such as Automatic Vehicle Location (AVL), are used in combination by practitioners and researchers to gain a deeper understanding of the public transit network. In some cases, AVL data are not available due to records being missing in the system. In such cases, people resort to the used schedule timetable such as General Transit Feed Specification (GTFS) to match smart card data to the transit network. Since delays or changes to the timetable are not contained in the scheduled timetable, it can result in wrong matches between the smart card data and the transit network. This paper shows how the uncertainty of arrival and departure times affects passengers to train assignments and proposes a method for estimating the missing arrival time of trains when the recorded timetable information is not available. The method uses the knowledge of how the tap-outs are distributed in a hierarchical, latent Bayesian model to predict the arrival times of trains. Evaluated on 15,136 train arrivals, the model can infer 70% of the arrivals times with an average error of 28 to 32 seconds depending on the station.

中文翻译：

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火车什么时候到站？使用智能卡数据丰富时间表信息的贝叶斯方法


从业者和研究人员结合使用自动收费系统 (AFC) 的智能卡数据和自动车辆定位 (AVL) 等时刻表信息，以更深入地了解公共交通网络。在某些情况下，由于系统中缺少记录，AVL 数据不可用。在这种情况下，人们求助于已使用的时间表，例如通用交通馈送规范（GTFS），以将智能卡数据与交通网络相匹配。由于时间表的延迟或更改不包含在预定时间表中，因此可能导致智能卡数据与交通网络之间的错误匹配。本文展示了到达和出发时间的不确定性如何影响乘客对火车分配的影响，并提出了一种在记录的时刻表信息不可用时估计火车错过到达时间的方法。该方法利用分层潜在贝叶斯模型中分接器如何分布的知识来预测火车的到达时间。通过对 15,136 趟列车到站进行评估，该模型可以推断出 70% 的到站时间，平均误差为 28 至 32 秒，具体取决于车站。