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[Background]With the large-scale construction and increasingly integrated operation of metro networks, metro ridership has grown rapidly. Passenger travel demands and patterns have become increasingly complex and diverse, presenting new challenges for metro operations and management. [Objective] Leveraging the advantages of mobile signaling data in continuously tracking users' travel trajectories, metro travel episodes are identified on the basis of the layout and coverage of base stations within metro stations, combined with thresholds like travel activity time. Next, typical metro travel patterns are mined to support metro optimization. [Method] Based on the metro network topology model and Dijkstra algorithm, passenger travel paths are reconstructed to obtain detailed daily travel records. A two-step classification method is then employed to uncover the heterogeneity of passenger travel behavior. Specifically, the users are divided into high-and low-frequency groups on the basis of their travel frequencies. Subsequently, using indicators such as temporal regularity, spatial distribution, and route utilization, the K-means++ clustering algorithm is employed to further refine the segmentation of each group. [Data] The study uses mobile signaling data from Shanghai in May 2019, which include 400 million signaling records generated by 4.48 million metro users. [Conclusions] The analysis extracts 30.09 million metro trips from 3.83 million users. Highfrequency users(18% of the total) contributed 67% of all trips, whereas low-frequency users(82%)accounted for only 33%. High-frequency users can be classified into three groups: commuters relying on a single route, commuters with flexible route choices, and regular users traveling for noncommuting purposes. Low-frequency users can be classified into three categories: business, leisure and entertainment, and single-day or transit travelers. The findings can inform resource allocation, targeted marketing strategies, and help improve operational efficiency in metro systems.
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Basic Information:
DOI：10.19961/j.cnki.1672-4747.2025.07.026
China Classification Code:U293.6
Citation Information:
[1]LIU Xiaolei,ZOU Guojian,DUAN Zhengyu ,et al.Individual-level metro route extraction and travel behavior pattern mining[J].Journal of Transportation Engineering and Information,2026,24(01):15-24.DOI:10.19961/j.cnki.1672-4747.2025.07.026.
Fund Information:
上海市“科技创新行动计划”社会发展科技攻关项目（20dz1202903）