Existing microscopic traffic models represent the lane-changing maneuver as a continuous and uninterrupted lateral movement of the vehicle from its original to the target lane. We term this representation as Continuous Lane-Changing (CLC). Recent empirical studies find that not all lane-changing maneuvers are continuous; the lane-changer may pause its lateral movement during the maneuver resulting in a Fragmented Lane-Changing (FLC). We analysed a set of 1064 lane changes from NGSIM dataset which contains 270 FLCs. In comparison to a CLC, this study investigates the distinction of an FLC in terms of its execution and its effects on neighbouring vehicles. We find that during the execution of an FLC, the lane-changer exhibits distinct kinematics and takes a longer duration to complete the lane-changing. We propose a trajectory model to describe the lateral kinematics during an FLC. Additionally, we find that the FLC induces a distinct effect on the follower in the target lane, and propose a model to describe the transient behavior of the target-follower during an FLC. The modelling results suggest that the accuracy of traffic flow models can be improved by deploying lane change execution and impact models that are specific to FLC and CLC. Besides, this study identifies a set of factors that might be related to the decision-making process behind FLC: an average driver executes an FLC when the preceding and following vehicles in the target lane are slower, and when the follower in the target lane is closer than those observed during the onset of a CLC. Our findings suggest that FLC is motivated by an increased necessity to change lane such as during a mandatory lane change.

中文翻译：

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零散车道变化的经验和模型

现有的微观交通模型将换道演习表示为车辆从其原始车道到目标车道的连续且不间断的横向运动。我们将此表示称为连续车道变更（CLC）。最近的经验研究发现，并非所有的变道演习都是连续的。换道器可能会在操纵过程中暂停其横向运动，从而导致换车道（FLC）碎片化。我们从NGSIM数据集中分析了1064个车道变化集，其中包含270个FLC。与CLC相比，本研究从FLC的执行及其对相邻车辆的影响方面研究了FLC的区别。我们发现在执行FLC期间，换道器表现出独特的运动学特性，并且需要更长的时间才能完成换道。我们提出了一个轨迹模型来描述FLC期间的横向运动学。此外，我们发现FLC对目标车道中的追随者产生了明显的影响，并提出了一个模型来描述目标跟随者在FLC期间的瞬态行为。建模结果表明，可以通过部署针对FLC和CLC的车道变更执行和影响模型来提高交通流模型的准确性。此外，本研究还确定了一系列可能与FLC背后的决策过程相关的因素：当目标车道中的前车和后车较慢时，以及目标车道中的跟随者为后车时，普通驾驶员执行FLC。比CLC发作期间观察到的结果更接近。