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

Abstract

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 have two critical issues in risk analysis, including ignoring crash impact and using unnormalized data. This paper applies an emerging hot spot detection method, called the prospective space-time scan statistic (STSS) method, for assessing the crash risk while incorporating the two overlooked issues at a refined spatial scale and over an extended time period. Specifically, we have applied the STSS method to a seven-year crash dataset in Hartford, Connecticut, and have derived the network-based crash risk. 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.