Lane-changing models are essential components for microscopic simulation. Although the literature recognizes that different classes of vehicles have different ways of performing lane-change maneuvers, lane change behavior of truck drivers is an overlooked research area. We propose that truck drivers are heterogeneous in their lane change behavior too and that inter-driver differences within truck drivers exist. We explore lane changing behavior of truck drivers using a trajectory data set collected around motorway bottlenecks in the Netherlands which include on-ramp, off-ramp, and weaving sections. Finite mixture models are used to categorize truck drivers with respect to their merging and diverging maneuvers. Indicator variables include spatial, temporal, kinematic, and gap acceptance characteristics of lane-changing maneuvers. The results suggest that truck drivers can be categorized into two and three categories with respect to their merging and diverging behaviors, respectively. The majority of truck drivers show a tendency to merge or diverge at the earliest possible opportunity; this type of behavior leads to most of the lane change activity at the beginning of motorway bottlenecks, thus contributing to the raised level of turbulence. By incorporating heterogeneity within the lane-changing component, the accuracy and realism of existing microscopic simulation packages can be improved for traffic and safety-related assessments.

车道变换模型是微观仿真的重要组成部分。尽管文献认识到不同类别的车辆具有执行换道操纵的不同方式，但是卡车驾驶员的换道行为是一个被忽视的研究领域。我们提出卡车司机在换道行为上也存在异质性，并且卡车司机内部存在驾驶员之间的差异。我们使用在荷兰的高速公路瓶颈周围收集的轨迹数据集（包括匝道，匝道和交织路段）来探索卡车驾驶员的变道行为。有限混合模型用于根据合并和分散操作对卡车驾驶员进行分类。指标变量包括变道演习的空间，时间，运动学和间隙接受特性。结果表明，就其合并和分散行为而言，卡车驾驶员可分为两类和三类。大多数卡车司机倾向于尽早合并或分叉。这种类型的行为导致高速公路瓶颈开始时的大多数车道变更活动，从而加剧了湍流。通过将异质性纳入换道组件中，可以改善现有微观仿真程序包的准确性和真实性，以进行与交通和安全相关的评估。这种类型的行为导致高速公路瓶颈开始时的大多数车道变更活动，从而加剧了湍流。通过将异质性纳入换道组件中，可以改善现有微观仿真程序包的准确性和真实性，以进行与交通和安全相关的评估。这种行为导致高速公路瓶颈开始时的大多数车道变更活动，从而加剧了湍流。通过将异质性纳入换道组件中，可以改善现有微观仿真程序包的准确性和真实性，以进行与交通和安全相关的评估。