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.

通过高分辨率车辆轨迹数据，该研究聚焦于车辆群体作为分析主体，探索了车辆群体和道路段特征对风险形成和传播机制的影响，并确定了多个影响事故风险的关键因素。研究开发了多项式逻辑回归模型对空间风险传播模式进行分析，结果显示高风险状态持续时间、车辆群体规模增加和频繁的变道与不利的风险传播模式相关联。此外，该研究对不同类型的分类器、预测时间间隔和自适应TTC阈值进行了敏感性分析，并获得了超过0.93的最高AUC值，为研究人员和实践者在理解和预测车辆群体安全方面提供了有价值的见解，进而改进车联网和自动驾驶车辆的交通安全管理和运营。

基于车辆群体的高速公路事故风险形成和传播分析