Identifying snowplow truck crash hotspots and spatial analysis of crashes in the mountainous roadways

Abstract

Analysis of spatial patterns can provide an efficient answer to the problem of locating global or local patterns of the spatial distribution of traffic crashes. Approximately 21% of vehicle crashes in the United States occur due to inclement weather, costing the U.S. economy more than $217.5 billion yearly. One major road winter maintenance activity is snowplow and spreading salt on the road surface to improve the driving condition. The potential for rear-end collisions or conflicts between motorists and Snowplow Trucks (SPTs) is a major safety concern. This study extensively applies Ripley’s K-function, the global Moran’s I measure and the Getis–Ord Gi* function along with Kernel Density Estimation and Network-based Kernel Density Estimations with the aim of analysing snowplow-involved crash hotspots in the state of Wyoming. The positive Moran’s I, the high z-scores and the small p values indicate that Snowplow truck crashes were spatially clustered.

Keywords:

• Crash hotspot analysis
• Snowplows
• Network Kernel Density Estimate
• Geographic Information System
• winter road maintenance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work presented in this paper is sponsored by the Wyoming Department of Transportation (contract number RS08221) and supported by the Mountain Plains Consortium (MPC). The subject matter, all figures, tables and equations listed in this paper will be included in the final report at the conclusion of this study. All rights reserved copy-righted in 2022, Wyoming Department of Transportation and the University of Wyoming.