The frequent crash occurrences have caused massive loss of lives and properties all over the world. In order to improve traffic safety, it is vital to understand the relationships between traffic operation conditions and crash risk, and further implement safety countermeasures. Emerging studies have conducted the crash risk analyses using discrete and aggregated traffic data (e.g., loop detector data, probe vehicle data), where crash events were selected as the prediction target. However, traditional traffic sensing data obtained at segment level cannot describe the detailed operation conditions for the vehicle platoons near crash locations. Thus, more microscopic and high-resolution traffic sensing data are needed. In addition, considering the random occurrence feature of crashes, high-risk events should be paid more attentions given their higher occurrence probability and consistent causations with crashes, which could proactively reduce crash likelihood. In this study, HighD Dataset from German highways was utilized for the empirical analyses. First, high-risk events were obtained using safety surrogate measures with Modified Time to Collision (MTTC) less than 2 s. Traffic operation characteristics within 5 s prior to event occurrence were extracted based on vehicle trajectory data. Then, a total of three different logistic regression models were established, which are standard logistic regression model, random-effects logistic regression (RELR) model, and random-parameter logistic regression (RPLR) model. Among which, the RPLR model was showed to have the best fitness and prediction accuracy. The results showed that the disturbed traffic flows in both longitudinal and lateral directions have positive impacts on high-risk events occurrence. Besides, too close following distance between vehicles would lead to high-risk events. Moreover, RPLR models could provide a high prediction accuracy of 97 % for 2 s ahead of the high-risk events. Finally, potential safety improvement countermeasures and future application scenarios were also discussed.

频繁发生的撞车事故已在世界范围内造成重大人员伤亡和财产损失。为了提高交通安全，了解交通运行状况与碰撞风险之间的关系，并进一步实施安全对策至关重要。新兴的研究已经使用离散和汇总的交通数据（例如，环路检测器数据，探测车辆数据）进行了碰撞风险分析，其中碰撞事件被选为预测目标。但是，在路段级别获得的传统交通传感数据无法描述碰撞地点附近车辆排的详细运行条件。因此，需要更多的微观和高分辨率交通感测数据。另外，考虑到崩溃的随机发生特征，鉴于高风险事件的发生概率较高，并且与崩溃的因果关系一致，因此应给予更多关注，这可以主动降低崩溃的可能性。在这项研究中，来自德国高速公路的HighD数据集被用于实证分析。首先，使用安全替代措施（修改碰撞时间（MTTC）少于2 s）获得高风险事件。根据车辆轨迹数据提取事件发生前5 s内的交通运营特征。然后，总共建立了三种不同的逻辑回归模型，分别是标准逻辑回归模型，随机效应逻辑回归（RELR）模型和随机参数逻辑回归（RPLR）模型。其中，RPLR模型具有最佳适应性和预测准确性。结果表明，纵向和横向扰动的交通流对高风险事件的发生有积极的影响。此外，车辆之间的追随距离太近会导致高风险事件。此外，RPLR模型可以在高风险事件发生前2秒钟提供97％的高预测精度。最后，讨论了潜在的安全改进对策和未来的应用场景。