Urban Adaptive Travel Hotspot Detection and Area Division Method

To address the limitations of fixed bandwidth hotspot detection in multi-density travel data and the spatial heterogeneity distortion from traditional zoning methods, this study proposes an adaptive hotspot detection and dy-namic regional division method for urban transportation. An adaptive travel density estimation model is developed. A global initial bandwidth is determined via pilot estimation, and sensitivity parameters are calibrated using maxi-mum likelihood estimation. Local bandwidth correction factors and a dynamic bandwidth adjustment mechanism en-able automatic research bandwidth regulation. A multilevel hotspot identification technology is developed, combin-ing moving window extreme value detection with natural breaks classification to form a travel hotspot evaluation system. Furthermore, Voronoi polygons are generated using the hotspots as control points to serve as basic analysis units while preserving spatial heterogeneity characteristics. The effectiveness of regional division is evaluated using 5 indicators, including travel heat, Moran's index, and others. Empirical analysis using Harbin's main urban area taxi trajectory data shows that, compared with fixed bandwidth methods, the proposed method increases identified hotspots by 2.1 to 6.7 times. The standard deviation of regional travel heat differences is 572.8. The average dis-tance between point data centroids within areal elements and the geometric center is 137.8 m, a 15.1% to 74.3% re-duction from traditional grid methods, verifying the homogeneity advantage of the regional division. The nugget to sill ratio drops to 0.135, and internal unit variation decreases by 39.6%, indicating the method effectively retains da-ta aggregation characteristics and reduces the modifiable areal unit problem's impact. The adaptive method identifies 1, 719 travel hotspots in the study area and accurately locates road intersection hotspots in areas like Harbin West Station, with clear boundaries and explicit geographical semantics. The results provide an adaptive framework for multi-density travel data analysis, supporting applications such as taxi dispatching and demand forecasting.