关键词: 电动汽车, 行人/非机动车事故, 时空分布特征, 事故多发点段鉴别, 聚类算法

Abstract:

To explore the spatiotemporal characteristics of collision accidents between electric vehicles and vulnerable road users under mixed traffic conditions, this study proposes a method for identifying spatiotemporal hot spots of collisions between electric vehicles and pedestrians/non-motorized vehi-cles. First, based on collision data involving electric vehicles and pedestrians/non-motorized vehicles, the Analytic Hierarchy Process is employed to determine the weights of the influencing fac-tors. Subsequently, a weighted kernel density estimation method reveals the spatial clustering of traf-fic accident locations. Building on this foundation, the Density Peaks Clustering algorithm is utilized as a spatial clustering model for accidents, and a Spatial Temporal-DBSCAN model is constructed to incorporate the temporal dimension, thereby accurately characterizing the spatiotemporal distribution characteristics of collision accidents between electric vehicles and pedes-trians/non-motorized vehicles. Finally, an empirical analysis is conducted using data from a city over a continuous period of 11 months regarding electric vehicle accidents. The research findings indicate that collisions between electric vehicles and pedestrians/non-motorized vehicles exhibit three tem-poral peaks, differing from the traditional bimodal trend observed in traffic accidents, while spatially, accidents display localized clustering characteristics. In terms of spatial hot spot identification, the DPC model outperforms the DBSCAN, OPTICS, and MeanShift algorithms, showing improvements of 42.9%, 74.5%, and 11.1% in silhouette coefficient, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI), respectively. For spatiotemporal hot spot identification, under comparable DBI conditions, the ST-DBSCAN model achieves silhouette coefficient and CHI values that are 2.25 times and 57.3% higher, respectively, than the best values obtained from ST-OPTICS, ST-DPC, and ST-MeanShift algorithms.

Key words: electric vehicles, pedestrian/non-motorized vehicle accidents, spatio-temporal distribution character-istics, accident-prone locations identification, clustering algorithm