Road Traffic Accident Hotspot Detection: A GIS-Based Machine Learning Approach Using HDBSCAN and Spatial Clustering Techniques

Road Traffic Accidents (RTAs) represent a significant public safety issue in rapidly urbanising nations, resulting in considerable fatalities, injuries, and economic losses. This research investigates the spatio-temporal distribution and hotspot dynamics of RTAs in Siliguri City, India, a principal transnational transport corridor connecting northeastern India with adjacent countries. A geocoded dataset comprising RTA incidents from 2021 to 2023 was analysed using integrated GIS-based machine learning and statistical methods. Temporal clusters were identified through Kulldorff’s purely temporal scan statistics, while Kernel Density Estimation (KDE) quantified accident density during morning peak, midday/off-peak, evening peak, and lean/night-time intervals. Spatial clustering was further assessed using LISA-Moran’s I, purely spatial scan statistics, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Emerging Hotspot Analysis (EHA) was employed to detect evolving hotspot patterns over time. The findings indicate that major accident hotspots are concentrated at key intersections and transport corridors, such as Hill Cart Road, Darjeeling More, Sevoke Road, Eastern Bypass, and Burdwan Road. Moran’s I (0.157; p = 0.007) demonstrates significant but moderate spatial autocorrelation, and spatial scan statistics identified three principal high-risk zones. HDBSCAN classified 81.90% of incidents within clustered areas. Lean/night-time periods exhibited the highest accident densities, reaching 14.21 accidents/km2 at critical intersections. These results underscore the utility of integrating GIS and machine learning techniques for urban traffic safety planning and hotspot-focused intervention strategies.