Routing controllability of autonomous vehicles (AVs) has been shown to reduce the impact of selfish routing on network efficiency. However, the assumption that AVs would readily allow themselves to be controlled externally by a central agency is unrealistic. In this paper, we propose a joint routing and pricing control scheme that aims to incentivize AVs to seek centrally controlled system optimal (SO) routing by saving on tolls while user equilibrium (UE) seeking AVs and human-driven vehicles (HVs) are subject to a congestion charge. The problem is formulated as a bi-level optimization, in which dynamic tolls are optimized in the upper level, whereas the lower level is a mixed equilibrium simulation-based dynamic traffic assignment model considering mixed fleet of AVs and HVs. We develop a feedback-based controller to implement a second-based pricing scheme from which SO-seeking AVs are exempt; but UEseeking vehicles, including both AVs and HVs, are subject to a distance-based charge for entering a pricing zone. This control strategy encourages controllable AVs to adopt SO routing while discouraging UE-seeking users from entering the pricing zone. We demonstrate the performance of the proposed framework using the Nguyen network and a large-scale network model of Melbourne, Australia.

中文翻译：

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基于混合平衡模拟的动态交通分配中自动驾驶汽车的联合路由和定价控制

自动驾驶汽车 (AV) 的路由可控性已被证明可以减少自私路由对网络效率的影响。然而，认为自动驾驶汽车很容易让自己受到中央机构外部控制的假设是不现实的。在本文中，我们提出了一种联合路由和定价控制方案，旨在通过节省通行费来激励 AV 寻求中央控制系统最优 (SO) 路由，同时用户均衡 (UE) 寻求 AV 和人类驾驶车辆 (HV)到拥堵费。该问题被表述为一个双层优化，其中在上层优化动态通行费，而下层是考虑到 AV 和 HV 混合车队的基于混合均衡模拟的动态交通分配模型。我们开发了一种基于反馈的控制器来实施基于秒的定价方案，寻求 SO 的 AV 可以免除；但是 UEseeking 车辆，包括 AV 和 HV，进入定价区需要按距离收费。这种控制策略鼓励可控制的 AV 采用 SO 路由，同时阻止寻求 UE 的用户进入定价区。我们使用 Nguyen 网络和澳大利亚墨尔本的大规模网络模型证明了所提出框架的性能。