Identification of high-risk expressway segments using connected vehicle data: an empirical analysis

Traditional road safety analysis primarily relies on historical crash data, which require long accumulation periods and are constrained by limitations such as insufficient data volume, imprecise location information, and underreporting, potentially leading to biased or delayed assessments of road safety risks. The emergence of connected vehicle (CV) technology provides new opportunities for more timely safety analysis. CVs are equipped with onboard sensors that monitor driving behavior and issue critical warnings, including headway monitoring warnings (HMWs) and forward collision warnings (FCWs). This study aims to proactively identify high-risk expressway segments using CV warning data. Accordingly, an integrated framework is developed, combining spatial hotspot identification and statistical regression modeling. Based on CV data from nine expressways in Shanghai, warning hotspots are identified using Moran's I and Getis-Ord Gi*, indicating locations with spatial clustering of HMWs and FCWs. The relationship between warning frequency and the number of collisions is examined through Poisson and Negative Binomial models estimated with and without incorporating CV warning frequencies as explanatory variables. To address the potential endogeneity between traffic conflicts and collisions, an instrumental variable Poisson model is further employed. The results confirm that HMW and FCW frequencies are positively associated with collisions, and that accounting for endogeneity improves estimation robustness. In addition, hotspot co-occurrence analysis and statistical testing reveal that segments identified exclusively as CV warning hotspots still experience significantly more collisions compared to segments identified as neither warning nor collision hotspots. This suggests that CV warning data can support early detection of emerging safety risks. This study contributes a structured and empirically supported framework that advances the application of connected vehicle data in proactive traffic risk assessment.