Previous studies in predicting crash risk primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics of the segment, usually neglecting the impact of vehicles' continuous movement and interactions with nearby vehicles. Advancements in communication technologies have empowered driving information collected from surrounding vehicles, enabling the study of group-based crash risks. Based on high-resolution vehicle trajectory data, this research focused on vehicle groups as the subject of analysis and explored risk formation and propagation mechanisms considering features of vehicle groups and road segments. Several key factors contributing to crash risks were identified, including past high-risk vehicle-group states, complex vehicle behaviors, high percentage of large vehicles, frequent lane changes within a vehicle group, and specific road geometries. A multinomial logistic regression model was developed to analyze the spatial risk propagation patterns, which were classified based on the trend of high-risk occurrences within vehicle groups. The results indicated that extended periods of high-risk states, increase in vehicle-group size, and frequent lane changes are associated with adverse risk propagation patterns. Conversely, smoother traffic flow and high initial crash risk values are linked to risk dissipation. Furthermore, the study conducted sensitivity analysis on different types of classifiers, prediction time intervalsss and adaptive TTC thresholds. The highest AUC value for vehicle-group risk prediction surpassed 0.93. The findings provide valuable insights to researchers and practitioners in understanding and prediction of vehicle-group safety, ultimately improving active traffic safety management and operations of Connected and Autonomous Vehicles.

翻译：以往预测事故风险的研究主要将路段上的事故数量或可能性与交通参数或几何特征相关联，通常忽略了车辆连续运动及其与邻近车辆交互的影响。通信技术的进步使得从周围车辆收集驾驶信息成为可能，从而支持了基于群体的事故风险研究。基于高分辨率车辆轨迹数据，本研究以车辆群为分析主体，综合考虑车辆群及路段特征，探索了风险形成与传播机制。研究发现，过去的高风险车辆群状态、复杂的车辆行为、大型车辆占比高、车辆群内频繁换道以及特定的道路几何条件是导致事故风险的关键因素。建立了多项逻辑回归模型用于分析空间风险传播模式，该模式根据车辆群内高风险事件的变化趋势进行分类。结果表明，长时间持续的高风险状态、车辆群规模增大以及频繁换道与不利的风险传播模式相关；反之，更顺畅的交通流和较高的初始事故风险值则与风险消散相关。此外，研究还对不同类型分类器、预测时间间隔以及自适应TTC阈值进行了敏感性分析。车辆群风险预测的最高AUC值超过0.93。研究结果为研究人员和实践者理解与预测车辆群安全性提供了宝贵见解，最终有助于提升主动交通安全管理以及网联自动驾驶车辆的运营水平。