Autonomous Vehicles (AVs) are bringing challenges and opportunities to urban traffic systems. One of the crucial challenges for traffic managers and local authorities is to understand the nonlinear change in road capacity with increasing AV penetration rate, and to efficiently reallocate the Right-of-Way (RoW) for the mixed flow of AVs and Human Driven Vehicles (HDVs). Most of the existing research suggests that road capacity will significantly increase at high AV penetration rates or an all-AV scenario, when AVs are able to drive with smaller headways to the leading vehicle. However, this increase in road capacity might not be significant at a lower AV penetration rate due to the heterogeneity between AVs and HDVs. In order to investigate the impacts of mixed flow conditions (AVs and HDVs), this paper firstly proposes a theoretical model to demonstrate that road capacity can be increased with proper RoW reallocation. Secondly, four different RoW reallocation strategies are compared using a SUMO simulation to cross-validate the results in a numerical analysis. A range of scenarios with different AV penetration rates and traffic demands are used. The results show that road capacity on a two-lane road can be significantly improved with appropriate RoW reallocation strategies at low or medium AV penetration rates, compared with the do-nothing RoW strategy.

自动驾驶汽车（AV）给城市交通系统带来了挑战和机遇。交通管理人员和地方当局面临的关键挑战之一是，要了解随着自动驾驶汽车普及率的提高，道路通行能力的非线性变化，并为自动驾驶汽车和人力驱动车辆的混合流有效地重新分配路权（RoW）（ HDV）。现有的大多数研究表明，当自动驾驶汽车能够以较小的行驶距离驶向领先的车辆时，在高自动驾驶汽车普及率或全自动驾驶汽车场景下，道路通行能力将大大提高。但是，由于AV和HDV之间的异质性，道路通行能力的增加在较低的AV渗透率下可能并不明显。为了研究混合流条件（AV和HDV）的影响，本文首先提出了一个理论模型，以证明通过适当的行权重分配可以增加道路通行能力。其次，使用SUMO仿真对四种不同的RoW重新分配策略进行比较，以对数值分析中的结果进行交叉验证。使用了一系列具有不同AV渗透率和流量需求的方案。结果表明，与不采取行动的RoW策略相比，采用适当的RoW重新分配策略可以在低或中等AV渗透率下显着提高两车道道路的通行能力。使用了一系列具有不同AV渗透率和流量需求的方案。结果表明，与不采取行动的RoW策略相比，采用适当的RoW重新分配策略可以在低或中等AV渗透率下显着提高两车道道路的通行能力。使用了一系列具有不同AV渗透率和流量需求的方案。结果表明，与不采取行动的RoW策略相比，采用适当的RoW重新分配策略可以在低或中等AV渗透率下显着提高两车道道路的通行能力。