Analyzing hotspots of taxi passenger pick-up areas is advantageous for understanding the spatiotemporal distribution characteristics of urban residents' travel patterns. Based on sampled GPS data from taxis in Xi'an, this study explores the distribution patterns of taxi trips. It introduces the k-distance curve to enhance the DBSCAN spatial clustering algorithm for analyzing passenger pick-up and drop-off points. Through data mining, it identifies passenger hotspots, thereby providing information decision-making services for taxi operators and managers.
Exploration of spatiotemporal characteristics of taxi trips based on GPS data
International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China
Proc. SPIE ; 13575
28.04.2025
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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