Abstract:

Urban traffic hotspots refer to the areas where residents visit frequently. Travels of urban residents generate interactions between urban areas, which form a spatial interactive network. Existing research mainly focuses on hotspot extraction and their dynamic change, but there are few studies that examine the interaction of traffic hotspots and the spatial interaction network. Based on the taxi trajectory data in Wuhan City, we detect the urban traffic hotspots by spatiotemporal data field clustering and further analyze the spatial interaction among urban hotspots based on the complex network theory. The results show that: (1) There is a large amount of interaction between the hotspots on holidays, and less interaction between the hotspots on weekdays; (2) The nodes with great influence are the bus and train stations, airports, and so on, on holidays, and the nodes with great influence are normally residential communities, workplace, and so on, on weekdays; (3) The results of community detection found that there is more interaction across the Yangtze River on weekdays, and little interaction across the Yangtze River on holidays. The study results can provide a reference for traffic management to develop different management strategies and methods for holidays and workdays. Investigating the interaction of human behaviors with urban environment, transportation, and geographical regions from the viewpoint of spatial interaction network is an innovative interdisciplinary research field crossing geographical information science, management science, and humanities and social sciences

Key words: urban traffic hotspots, spatial interaction network, complex network, community detection, rajectory clustering, spatiotemporal data field, Wuhan City