Application of Geographical Information System Techniques to Determine High Crash-Prone Areas in the Fort Peck Indian Reservation

Abstract

Historically, Indian reservations have been struggling with higher crash rates than the rest of the United States. In an effort to improve roadway safety in these areas, different agencies are working to address this disparity. For any safety improvement program, identifying high risk crash locations is the first step to determine contributing factors of crashes and select corresponding countermeasures. This study proposes an approach to determine crash-prone areas using Geographic Information System (GIS) techniques through creating crash severity maps and Network Kernel Density Estimation (NetKDE). These two maps were assessed to determine the high-risk road segments having a high crash rate, and high injury severity. However, since the statistical significance of the hotspots cannot be evaluated in NetKDE, this study employed Getis-Ord Gi* (d) statistics to ascertain statistically significant crash hotspots. Finally, maps generated through these two methods were assessed to determine statistically significant high-risk road segments. Moreover, temporal analysis of the crash pattern was performed using spider graphs to explore the variance throughout the day. Within the Fort Peck Indian Reservation, some parts of the US highway 13, BIA Route 1, and US highway 2 are among the many segments being identified as high-risk road segments in this analysis. Also, although some residential roads have PDO crashes, they have been detected as high priority areas due to high crash occurrence. The temporal analysis revealed that crash patterns were almost similar on the weekdays reaching the peak at traffic peak hours, but during the weekend, crashes mostly occurred at midnight. The study would provide tribes with the tool to identify locations demanding immediate safety concerns. This study can be used as a template for other tribes to perform spatial and temporal analysis of the crash patterns to identify high risk crash locations on their roadways.