Urban Hotspot Area Detection Using Nearest-Neighborhood-Related Quality Clustering on Taxi Trajectory Data

Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It of great significance mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics the operational status traffic. The existing clustering methods mainly concentrate on number objects contained in within a specified size, neglecting impact local density tightness between objects. Hence, novel algorithm proposed detecting data based nearest neighborhood-related quality techniques. spatial not only considers maximum limited range but also relationship each cluster center its neighborhood, effectively addressing unevenly distributed datasets. As result, obtains high-quality results. visual representation simulated experimental results real-life cab dataset show suitable inferring areas, it better accuracy than traditional density-based methods.