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