Assessing network-based traffic crash risk using prospective space-time scan statistic method

As car ownership and urbanization continue to rise worldwide, traffic crashes have become growing concerns globally. Measuring crash risk provides insight into understanding crash patterns, which can eventually support proactive transport planning and improve road safety. However, traditional spatial analysis methods for crash risk assessment, such as the hotspot detection method, are mainly focused on identifying areas with higher crash frequency. These methods are subject to critical issues in risk analysis due to ignoring crash impacts and background traffic volume information. Aside from the two issues, current crash risk assessment methods, especially those aiming for cluster detection, are subject to the modified temporal unit problem, referring to the temporal effects (i.e., aggregation, segmentation, and boundary) in cluster detection. To alleviate these issues, this paper applies an emerging hot spot detection method, called the prospective space-time scan statistic (STSS) method, for assessing the crash risk at a refined network scale and over multiple years in a case study of Hartford, Connecticut. By identifying the spatial and temporal clusters of the crash risk, the study can provide evidence for tailoring road safety management strategies in neighborhoods characterized by high crash risk.