Vehicle bypassing is known to increase delays at traffic diverges. However, due to the complexities of this phenomenon, accurate and yet simple models of such lane change maneuvers are hard to develop. In this work, we present a macroscopic model for predicting the number of vehicles that perform a bypass at a traffic diverge when taking an exit link. We interpret the bypassing maneuver of vehicles at a traffic diverge as drivers acting selfishly; every vehicle selects lanes such that its own cost of travel is minimized. We discuss how we model the costs that are incurred by the vehicles. Then, taking into account the selfish behavior of vehicles, we model the lane choice of vehicles at a traffic diverge as a Wardrop equilibrium. We state and prove the properties of the equilibrium in our model. We show that there always exists an equilibrium for our model. Moreover, although our model is an instance of nonlinear asymmetrical routing games which in general have multiple equilibria, we prove that the equilibrium of our model is unique under certain assumptions that we observed to hold in all our case studies. We discuss how our model can be calibrated by running a simple optimization problem. Then, using our calibrated model, we validate it through simulation studies and demonstrate that our model successfully predicts the aggregate lane change maneuvers that are performed by vehicles at a traffic diverge. Having shown the predictive power of our model, we discuss how our model can be employed to obtain the optimal lane choice behavior of vehicles, where the social or the overall cost of all vehicles is minimized. Finally, we demonstrate how our model can be utilized in scenarios where a central authority can dictate the lane choice and trajectory of certain vehicles, for example autonomous vehicles directed by a central authority, so as to increase the overall vehicle mobility at a traffic diverge. Examples of such scenarios include the case when both human driven and autonomous vehicles coexist in the network.

已知车辆绕行会增加交通疏导时的延误。然而，由于这种现象的复杂性，难以开发这种车道变换动作的准确而简单的模型。在这项工作中，我们提出了一个宏观模型，用于预测在驶出出口时在交通岔道处绕行的车辆数量。我们将车辆在交通分歧时的绕行行为解释为驾驶员的自私行为；每辆车都选择车道，以使其自身的旅行成本降到最低。我们讨论如何建模车辆产生的成本。然后，考虑到车辆的自私行为，我们在交通分歧时将车辆的车道选择建模为Wardrop平衡。我们在模型中陈述并证明均衡的性质。我们证明了我们的模型总是存在一个平衡。而且，尽管我们的模型是非线性不对称路由博弈的一个实例，该博弈通常具有多重均衡，但是我们证明了在我们观察到的所有案例研究中都存在的某些假设下，模型的均衡是唯一的。我们讨论了如何通过运行一个简单的优化问题来校准我们的模型。然后，使用我们的校准模型，通过仿真研究对其进行验证，并证明我们的模型成功预测了车辆在交通偏离时执行的总车道变更操作。在展示了模型的预测能力之后，我们将讨论如何使用模型来获得车辆的最佳车道选择行为，从而使所有车辆的社会成本或总体成本降至最低。最后，我们演示了如何在中央主管部门可以指示某些车辆（例如由中央主管部门指挥的自动驾驶车辆）的车道选择和轨迹的场景中利用我们的模型，从而在出现交通分歧时提高整体车辆的机动性。这样的场景的示例包括人力和自动驾驶汽车在网络中共存的情况。