To explore in-depth the spatiotemporal distribution characteristics of collisions between electric vehicles and vulnerable road users, this paper proposes a method for identifying spatiotemporal hotspot segments of collisions between electric vehicles and pedestrians/non-motor vehicles. First, based on the collision data involving electric vehicles and pedestrians/non-motor vehicles, the analytic hierarchy process is employed to determine the weights of the influencing factors, and a weighted network kernel density estimation method is employed to reveal the spatial clustering of traffic accidents. On this basis, the density peaks clustering (DPC) algorithm is utilized as a spatial clustering model for accidents, and a spatiotemporal-DBSCAN (ST-DBSCAN) model is constructed to incorporate the temporal dimension, thereby accurately characterizing the spatiotemporal distribution characteristics of collision accidents between electric vehicles and pedestrians/non-motor vehicles. Finally, an empirical study is conducted using the electric vehicle accident data from a city over 11 consecutive months. The results indicate that the collisions between electric vehicles and pedestrians/non-motor vehicles exhibit 3 temporal peaks, differing from the traditional bimodal characteristic observed in traffic accidents, while spatially demonstrating localized clustering characteristics. For identifying spatial hotspot segments, as compared with the optimal values of DBSCAN, OPTICS and Mean Shift algorithms, DPC algorithm shows improvements of 42.9%, 74.5% and 11.1% in terms of silhouette coefficient, Davies-Bouldin index (DBI) and Calinski-Harabasz Index (CHI), respectively. For identifying spatiotemporal hotspot segments, under similar DBI conditions, ST-DBSCAN algorithm achieves silhouette coefficient and CHI values that are 2.25 times and 57.3% higher, respectively, than the optimal values of ST-OPTICS, ST-DPC and ST-Mean Shift algorithms.

With the rapid development of the road transport industry, the number of accidents involving operational vehicles is continuously increasing. Especially, operational vehicles, due to their unique operational characteristics, face more complex traffic environments and risks. This study analyzes the impact of unobserved heterogeneity on the severity of operational vehicle accidents. Based on traffic accident data of operational buses and trucks in China, relevant variables are selected from such four aspects as driver behavior, vehicle type, road characteristics and environmental conditions, and a random parameter Logit model is constructed. By introducing random parameters, the model can effectively capture the heterogeneity and uncertainty between individuals, thus improving its explanatory power and predictive performance. The SHAP method is further applied to analyze the direction, importance, and non-linear interactions between variables. The results show that, for operational buses, complex road shapes and vehicle types significantly increase the severity of accidents, especially the interaction effect between complex road conditions and improper operations, which notably raises the accident severity. For operational trucks, the interaction effect between hazardous material transport vehicles and complex road conditions is stronger, and speeding behavior significantly increases the probability of major accidents. The SHAP analysis quantifies the contribution of 10 multidimensional factors to accident severity, revealing that bus accidents are mainly influenced by road environment factors, while truck accidents are more significantly related to vehicle attributes. This further quantifies the differing impacts of human factors and environmental factors on the severity of accidents.