Gu Jinjing, Jiang Zhibin, Sun Yanshuo, Zhou Min, Liao Shenmeihui, Chen Jingjing
Department of Computer Science & Technology and Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China.
College of Transportation Engineering, Tongji University, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China.
Knowl Based Syst. 2021 Jan 9;211:106528. doi: 10.1016/j.knosys.2020.106528. Epub 2020 Oct 16.
This study presents a methodology for estimating passenger's spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger's entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the -gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers' unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified.
本研究提出了一种利用城市轨道交通网络中不完整的Wi-Fi探测数据，以个性化和及时性来估计乘客时空轨迹的方法。与仅记录乘客进出站情况的自动售检票数据不同，Wi-Fi探测数据能够捕捉更详细的乘客移动信息，比如乘坐列车或在站台等候。然而，由于一些不利情况可能导致数据不足，时空轨迹的估计仍然是一项具有挑战性的任务。为解决这个问题，我们首先描述Wi-Fi探测数据并总结其常见缺陷。然后，开发了 -gram方法来推断缺失的时空位置信息。接下来，设计了一种估计算法，通过整合多个数据源，即城市轨道交通网络拓扑、Wi-Fi探测数据、列车时刻表等，为每个乘客生成可行的时空轨迹。所提出的方法在盲实验中的模拟数据和来自复杂城市轨道交通网络的真实数据上都进行了测试。案例研究结果表明，93%的乘客独特物理路线能够被估计出来。然后，对于80%的乘客，可行的时空轨迹数量可以减少到一两条。还确定了轨迹估计方法的潜在应用。
