Vehicles often move forward in groups on the highways, especially when speed and density are high simultaneously. Abnormal maneuvers of a vehicle in a group influence multiple vehicles surrounding it, potentially leading to traffic accidents. We propose an approach to identify vehicle groups and analyse the factors influencing their evolutions using vehicle trajectory data. The proposed approach quantifies the interactions between neighboring vehicles based on the potential energy field, represents the interactive relationships among multiple vehicles using a multi-vehicle interaction network, and adopts the process of sub-network segmentation to identify vehicle groups. A random-parameter logistic regression (RPLG) model is developed to examine the influence of vehicle group features on vehicle group split. The effectiveness of the proposed approach is demonstrated in a case study using a real-world dataset. The case study reveals that: (1) the interaction strengths between vehicles tend to increase with increasing speed, (2) the interaction strength between a vehicle and its preceding vehicle is the largest, while the interaction strengths between a vehicle and its vehicles on its sides are the lowest, and (3) higher longitudinal and lateral speeds, larger fluctuations in longitudinal speeds and accelerations, larger group size, larger distances between vehicles, more lanes occupied by a vehicle group, and higher vehicle interactions significantly increase the probability of vehicle group split. The findings of this study can potentially support the traffic management and development of autonomous driving technology in connected vehicle environments.

中文翻译：

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使用车辆轨迹数据进行车辆组识别和进化分析

车辆经常在高速公路上成群前进，尤其是当速度和密度同时很高时。群体中车辆的异常操纵会影响其周围的多辆车辆，可能导致交通事故。我们提出了一种方法来识别车辆组并使用车辆轨迹数据分析影响其演变的因素。该方法基于势能场量化相邻车辆之间的交互，使用多车辆交互网络表示多车辆之间的交互关系，并采用子网络分割过程来识别车辆组。开发了随机参数逻辑回归（RPLG）模型来检查车辆组特征对车辆组分裂的影响。使用真实世界数据集的案例研究证明了所提出方法的有效性。案例研究表明：（1）车辆之间的相互作用强度随着速度的增加而增加；（2）本车与前车之间的相互作用强度最大，而本车与其后车之间的相互作用强度（3）纵向和横向速度较高，纵向速度和加速度波动较大，群体规模较大，车辆之间的距离较大，车队占用的车道较多，车辆交互作用较高，显着增加了车辆发生碰撞的概率。小组分裂。这项研究的结果可能支持联网车辆环境中的交通管理和自动驾驶技术的开发。