Visualizing Traffic Accident Hotspots Based on Spatial-Temporal Network Kernel Density Estimation

Benjamin Romano, Zhe Jiang

Abstract: Understanding where traffic accidents occur is crucial for improving road safety and proper traffic enforcement allocation. One of the most common methods of analyzing traffic accidents is spatial hotspot detection. Existing hotspot detection methods, e.g., spatial scan statistics, spatial and spatiotemporal kernel density estimation, mostly focus on Euclidean space. These methods ignore an important aspect of traffic accident hotspots, i.e., traffic accident locations are constrained to road networks. Several techniques have been proposed to detect spatial hotspot on the network space, including network kernel density-estimation, and significant linear route detection, but the time dimension and temporal dynamics of hotspots are not incorporated. To address the limitations of existing methods, we demonstrated a new method called Spatial-Temporal Network Kernel Density Estimation (STNKDE) that integrates both of these features. We also developed a prototype system and visualized the dynamics of traffic accident hotspots in New York City 2017.