Identification of Traffic Accident Hotspots using Network Kernel Density: Application to Galle District in Sri Lanka

This study aims to identify traffic accident hotspots and establish the correlation between road features and accidents. Network-based Kernel Density Estimators (NKDE) were computed using traffic accident data from 2009 to 2013 for each year for road segments termed lixels. These yearly NKDEs were aggregated to formulate a traffic accident intensity index (AII), serving as the foundation for hotspot identification. A Gamma regression model with a logarithmic link was constructed to relate the AII values of lixels in the A2 highway with macro-level parameters. Population, Junction density (number of junctions in a section), Bendiness, and Urbanisation. The study determined that a cell size of 250 m with a 500 m bandwidth is optimal for NKDE calculations for the road network of the Galle district. Lixels having extreme AII values were defined as the traffic accident hotspots and subdivided into yellow, orange, and red zones. Additionally, the Gamma regression model highlighted significant correlations between road attributes and accident intensity, indicating a 131% increase in AII values in urban areas compared to rural ones. Areas with junctions and bends had a decreasing effect on the AII values. With the increase of a junction, AII decreased significantly by 29 %, and if a bend is present in the lixel, AII decreases significantly by 12%.