高速公路合流区的车辆换道频繁、驾驶环境复杂、交通冲突严重，是交通事故的多发区域，准确理解高速公路合流区车辆运行状态与交通安全之间的关系，能够为实时预判事故风险、构建交通安全控制策略提供依据。本研究基于德国ExiD车辆轨迹数据，根据车辆进入加速车道到汇入主线过程中主线外侧车辆的相对位置变化，对高速公路合流区车辆汇入模式进行划分。为系统刻画汇入行为所带来的安全风险，引入碰撞时间（TTC）理论，构建了汇入风险表征体系。该体系包括两个层面的指标：一是基于二维TTC的汇入时刻风险指标，用于评估车辆在汇入瞬间的潜在冲突程度；二是基于碰撞暴露时间的汇入过程风险指标，用于评估整个汇入过程中的累积风险水平。在模型构建方面，采用XGBoost、LightGBM、GBDT和RF 4种机器学习算法，建立高速公路合流区车辆汇入风险判别模型，并通过SHAP（SHapley Additive exPlanations）理论对高速公路合流区车辆汇入风险致因进行分析。实验结果表明，基于XGBoost的高速公路合流区的汇入风险判别模型表现最优，总体准确率达95.52%，其准确率、精确率、召回率和F1分数方面均优于其他模型。此外，模型对比结果表明：考虑了汇入持续时间和汇入紧迫度的风险识别模型准确率更高。SHAP的分析进一步揭示：合流区车辆汇入风险与主线前车速度差平均值、速度差最大值、距离平均值、汇入持续时间、汇入过程纵向加速度标准差、汇入车辆速度等因素关系密切。

Expressway merging areas are characterized by frequent lane changes, complex driving environments, and intense traffic conflicts, making them high-risk zones for traffic accidents. Accurately understanding the relationship between vehicle operating status and traffic safety in these merging areas can provide a foundation for real-time accident risk prediction and the development of effective traffic safety control strategies. This study is based on vehicle trajectory data from the German ExiD dataset. By analyzing the changes in the relative positions of mainline outer-lane vehicles during the process from a vehicle entering the acceleration lane to merging into the mainline, the merging patterns in expressway merging areas were classified. To systematically describe the safety risks associated with merging, this study introduced the Time to Collision theory and developed a risk representation framework. This framework includes two levels of indicators: (1) a merging moment risk indicator based on two-dimensional TTC, which evaluates potential conflict at the moment of merging; and (2) a merging process risk indicator based on collision exposure time, which reflects the accumulated risk throughout the merging process. For model development, four machine learning algorithms—XGBoost, LightGBM, GBDT, and Random Forest—were used to build a classification model for merging risk. In addition, SHAP was applied to interpret the model and analyzed the key factors influencing merging risk. Experimental results show that the XGBoost-based risk identification model for expressway merging areas outperforms other models, achieving an overall accuracy of 95.52%. It also demonstrates superior performance in terms of accuracy, precision, recall, and F1-score. Furthermore, comparison among models indicates that incorporating merging duration and urgency significantly improves risk identification accuracy. SHAP analysis further reveals that merging risk is closely related to several factors, including the ave-rage and maximum speed differences with the leading vehicle on the mainline, the average distance to the leading vehicle, merging duration, the standard deviation of longitudinal acceleration during merging, and the speed of the merging vehicle.