1School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China 2Beijing Logistics Informatization Research Base, Beijing 100044, China 3Beijing Jingtou Yiyajie Transportation Technology Co., Ltd., Beijing 100101, China
[Objective] This study constructs data sets and algorithms to identify abnormal riding behaviour in urban rail transit (theft, begging, performing arts, and unauthorized advertisement distribution). [Methods] By constructing a spatiotemporal matrix, the passengers’ spatiotemporal trajectories are refined into the spatiotemporal feature map. All travel records are retained in the map without increasing complexity. Then, we used the spatiotemporal feature map as input to create an algorithm framework based on the attention mechanism and graph convolution neural networks. This algorithm can extract passengers’ key trajectory pattern features and identify abnormal behaviour from the regular passenger flow. [Results] Experimental results demonstrate the effectiveness of the proposed method, achieving a precision of 93.10%, a recall of 95.30%, and an F1 of 94.19%. All evaluation metrics have improved by over 3% compared to the baseline model. [Limitations] More research is needed to expand the sample size of the dataset and address the false positive issues. Our model cannot identify abnormal passengers who frequently change their smart cards. [Conclusions] This study constructs a dataset for abnormal commuting behavior with a larger sample size and reduced workload. The model can serve as a tool for accurately identifying abnormal commuting behavior in rail transit systems.
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