为了深入探究电动汽车与道路弱势群体碰撞事故的时空分布特征，提出一种电动汽车与行人/非机动车碰撞事故的时空多发点段鉴别方法。首先，基于电动汽车与行人/非机动车碰撞事故数据，采用层次分析法确定事故影响因素的权重，通过赋权网络核密度估计方法揭示交通事故的空间聚集性。在此基础上，采用密度峰值聚类（DPC）算法作为事故空间聚类模型，并引入带时间维度特征的时空DBSCAN（ST-DBSCAN）算法构建事故时空多发点段鉴别模型，精准刻画电动汽车与行人/非机动车碰撞事故的时空多发位置。最后，以某市连续11个月的电动汽车事故为例进行实证研究。结果表明：电动汽车与行人/非机动车碰撞事故在时间上具有3个高峰，异于传统交通事故的双峰特征，在空间则呈局部集中特征；对于空间多发点段的鉴别，相比DBSCAN、OPTICS和Mean Shift算法的最优值，DPC算法的轮廓系数、Davies-Bouldin指数（DBI）和Calinski-Harabasz指数（CHI）分别具有42.9%、74.5%和11.1%的优势；对于时空多发点段的鉴别，在DBI相近的条件下，ST-DBSCAN算法的轮廓系数和CHI分别比ST-OPTICS、ST-DPC和ST-Mean Shift算法的最优值高2.25倍和57.3%。

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.