Compared to human-driven vehicles (HDVs), connected and autonomous vehicles (CAVs) can drive closer to each other to enhance link capacity. Thereby, they have great potential to mitigate traffic congestion. However, the presence of HDVs in mixed traffic can significantly reduce the effects of CAVs on link capacity, especially when the proportion of HDVs is high. To address this problem, this study seeks to control the HDV flow using the autonomous vehicle/toll (AVT) lanes introduced by Liu and Song (2019). The AVT lanes grant free access to CAVs while allowing HDVs to access by paying a toll. To find the optimal toll rates for the AVT lanes to improve the network performance, first, this study proposes a multiclass traffic assignment problem with elastic demand (MTA-ED problem) to estimate the impacts of link tolls on equilibrium flows. It not only enhances behavioral realism for modeling the route choices of HDV and CAV travelers by considering their knowledge level of traffic conditions but also captures the elasticity of both HDV and CAV demand in response to the changes in the level of service induced by the tolls on AVT lanes. Thereby, it better estimate the equilibrium network flows after the tolls are deployed. Then, two categories of optimal toll design problems are formulated according to whether the solution of the HDV route flows, CAV link flows and corresponding origin–destination demand of the proposed MTA-ED problem is unique or not. To solve these optimal toll design problems, this study proposes a revised method of feasible direction. It linearizes the anonymous terms in the upper-level problem by leveraging the analytical sensitivity analysis results of the lower-level MTA-ED problem. This algorithm is globally convergent on the condition that the MTA-ED problem has a unique solution. It can also be leveraged to solve optimal toll design problems when the MTA-ED problem has multiple solutions. Numerical application found that due to disruptive effects on link capacity, using HDVs may significantly reduce the network performance such as customer surplus and total travel demand. The proposed method can assist different stakeholders to find the optimal toll rates for HDVs on AVT lanes to maximize the network performance under mixed traffic environments.

与人类驾驶的车辆（HDV）相比，联网和自动驾驶的车辆（CAV）可以彼此靠近，以增强链接能力。因此，它们具有缓解交通拥堵的巨大潜力。但是，混合流量中存在HDV可以显着降低CAV对链路容量的影响，尤其是在HDV的比例较高时。为了解决这个问题，本研究试图使用Liu和Song（2019）引入的自动驾驶汽车/收费（AVT）车道来控制HDV流量。AVT车道可免费访问CAV，同时允许HDV通过付费来访问。为了找到AVT车道的最佳通行费率以改善网络性能，首先，本研究提出了具有弹性需求的多类交通分配问题（MTA-ED问题），以估算链路通行费对均衡流量的影响。通过考虑交通状况的知识水平，它不仅增强了对HDV和CAV旅行者的路线选择进行建模的行为现实性，而且还捕获了HDV和CAV需求的弹性，以应对通行费造成的服务水平变化。 AVT车道。因此，它可以更好地估计在通行费部署后的平衡网络流量。然后，根据HDV路由流，CAV链路流的解决方案以及所提出的MTA-ED问题的相应起点-目的地需求是否唯一，来制定两类最优收费设计问题。为了解决这些最优的通行费设计问题，本研究提出了一种可行方向的修正方法。它利用较低级别的MTA-ED问题的分析灵敏度分析结果来线性化较高级别问题中的匿名术语。该算法在MTA-ED问题具有唯一解的条件下是全局收敛的。当MTA-ED问题有多种解决方案时，也可以利用它来解决最佳的通行费设计问题。数值应用发现，由于对链路容量的破坏性影响，使用HDV可能会大大降低网络性能，例如客户剩余和总旅行需求。所提出的方法可以帮助不同的利益相关者找到AVT车道上的HDV的最佳通行费率，以在混合交通环境下最大化网络性能。当MTA-ED问题有多种解决方案时，也可以利用它来解决最佳的通行费设计问题。数值应用发现，由于对链路容量的破坏性影响，使用HDV可能会大大降低网络性能，例如客户剩余和总旅行需求。所提出的方法可以帮助不同的利益相关者找到AVT车道上的HDV的最佳通行费率，以在混合交通环境下最大化网络性能。当MTA-ED问题有多种解决方案时，也可以利用它来解决最佳的通行费设计问题。数值应用发现，由于对链路容量的破坏性影响，使用HDV可能会大大降低网络性能，例如客户剩余和总旅行需求。所提出的方法可以帮助不同的利益相关者找到AVT车道上的HDV的最佳通行费率，以在混合交通环境下最大化网络性能。