Improving Bus Service Levels: A Coupling Coordination Model Based on Travel Hotspot Detection

Urban public transportation systems are vital for sustainable urban development, yet aligning bus service supply with dynamic travel demand remains challenging. Existing studies often overlook the spatial coupling relationship between regional bus services and actual travel patterns derived from trajectory data, particularly in addressing multiscale zoning and supply–demand imbalance identification. This study proposes an integrated framework combining taxi data mining, hotspot detection, and supply–demand coupling analysis to optimize bus service levels. Kernel density estimation and an adaptive travel hotspot detection model were applied to identify travel hotspots in Harbin, China, constructing spatial units with intraregional aggregation and interregional heterogeneity. A Voronoi diagram–based zoning method established bus service–oriented analysis units, while a multiparameter entropy-weighted model quantified bus supply levels. A coupling coordination degree model evaluated spatial matching between supply (bus infrastructure) and demand (travel intensity), complemented by Moran’s scatter plot for imbalance diagnosis. Results revealed that 30.9% of regions exhibited uncoordinated supply–demand relationships (coupling degree <0.5), primarily in suburban areas with insufficient bus coverage despite high taxi-derived demand. Central urban areas demonstrated moderate coordination (average coupling degree = 0.55), whereas newly developed zones showed significant service gaps. The hotspot-driven zoning method significantly outperformed traditional fixed-grid approaches in characterizing spatial heterogeneity, enabling precise identification of priority optimization areas. This highlights the need for route expansion and frequency adjustments in high-demand suburban regions. The proposed framework bridges trajectory analytics with transportation planning, offering actionable insights for data-driven public transit system optimization.