Spatial and Temporal Law Mining Method of Subway Commuting Behavior Based on Clustering


National Key Research and Development Program Project (2019YFB2102503)

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    The current method of dividing commuting groups takes less into account the time continuity characteristics of commuting trips. Based on the one-week subway card swipe data in Shanghai, this paper constructs a work-life recognition model for commuters, defines a commuting trip time similarity calculation method, and then extracts the features to classify commuter groups hierarchically, and uses the hot spot analysis model to perform spatial analysis and visual expression for spatial analysis and visualization, and explores the spatiotemporal regularity of commuters and the spatial distribution characteristics of work-housing organization characteristics in Shanghai. The results show that: (1) The employment single center model is obvious, and the employment hotspots of different clusters are distributed in the city center, and the settlements are characterized by the spatial organization of “hot in the west and cold in the east”. (2) The mainstream commute hours are 7:00—8:30 and 17:00—19:00, with nearly half of the commuters commuting during the morning rush hours and 90% leaving the work places before 19:30. (3) The travel time characteristics of the different commuting types are generally consistent with the distribution of their work and housing hotspots. The proposed research method reveals that the travel time law of commuters has a strong correlation with the spatial distribution of work-housing hotspots, which provides reference information for urban operation management and urban planning.

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LI Mingzhu, ZHAO Xizhi, CHEN Cai, ZHANG Fuhao, ZHU Jun, Qiu Agen. Spatial and Temporal Law Mining Method of Subway Commuting Behavior Based on Clustering[J]. Journal of Integration Technology,2023,12(1):79-90

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  • Online: January 12,2023
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