Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

Significant risk factors that influence the occurrence of heavy vehicle accidents have been explored in numerous studies in order to lower injury severity in traffic accidents. It is imperative to explore road sections with a high risk of heavy vehicle accident occurrence by considering the significant consequences of such accidents for road users, despite the low number of heavy vehicles in traffic flow. To address this, this study proposes a method to predict clustering hotspots for heavy vehicle accidents on the basis of three different criteria, namely, heavy vehicle accident cases, the number of heavy vehicles involved, and accident severity index values. Moran's I spatial autocorrelation was employed to identify the clustering for each criterion, and the Getis-Ord Gi* statistic was applied to estimate the likelihood of risk along the network. This study considers the features of hotspot points at significance levels from 0.10 to 0.01 with a 1355 m buffer radius to create segments for each criterion. The three criteria for hotspots were considered within the overlapped buffer zone. A total of 22 heavy vehicle risk segments (HVRSs) were identified and then ranked by crash rate. Overall, this study demonstrates the application of different criteria to identify accident hotspots involving a specific vehicle type, which could help in prioritizing segments with a high risk of heavy vehicle accidents, as well as providing information for HVRSs for the purpose of developing appropriate countermeasures for the identified accident hotspots.

Automated summary

This study proposes a method to predict clustering hotspots for heavy vehicle accidents by analyzing criteria such as heavy vehicle accident cases, number of heavy vehicles involved, and accident severity index values. By using Moran's I and Getis-Ord Gi* statistic, 22 heavy vehicle risk segments were identified within a certain buffer radius. This approach helps in prioritizing road segments with a high risk of heavy vehicle accidents and developing appropriate countermeasures for identified accident hotspots.