Hotspot analysis of single-vehicle lane departure crashes in North Dakota

Abstract

According to the North Dakota Department of Transportation (NDDOT), 90% of the state's fatal lane departure crashes between 2015 and 2019 occurred on rural roads. Of these, 77% were single-vehicle events. The objective here was to identify relatively high-risk areas on the rural road system. Spatial analysis techniques were explored as a beneficial tool in resource allocations aimed at single-vehicle crash prevention. Hotspot identification techniques, including Global Moran's I, local Moran's I, network kernel density estimation (NetKDE), and emerging hotspot analysis were employed. While the Global Moran's I index indicated the existence of crash clustering, the local Moran's I statistic revealed hot and cold spots in the state. The NetKDE approach was used to quantify crash clusters and prioritize locations. Results from NetKDE defined boundaries for each cluster in terms of density values embedded in the roadway. Emerging hotspot analysis evaluated the hot and cold spots with respect to time. This study will provide valuable insight and help decision makers to make more informed decisions with respect to education, enforcement and infrastructure strategies aimed at preventing single-vehicle lane departure crashes. Although limited to a narrow crash type in one state, this approach can inform other jurisdictions seeking to empirically visualize hotspots and more effectively deploy traffic safety strategies.