In a prolonged transitional period, the urban transportation infrastructure is expected to accommodate two different types of vehicles. One type of vehicle is the human-driven vehicle (HV) and the other is the connected and autonomous vehicle (CAV). Nevertheless, the conflict between HVs and CAVs in the mixed traffic scenario significantly impedes the efficiency-improvement benefit of implementing emerging CAV technologies. To harness the full potential of CAVs in enhancing traffic efficiency and network performance, multi-type lanes (i.e., regular lanes, dedicated CAV lanes, and CAV/toll lanes) and multi-type intersections (i.e., conventional signalized intersections, novel signalized intersections with an exclusive phase and exclusive approaches, and smart signal-free intersections) are proposed to efficiently manage the heterogeneous traffic flow on roads and at intersections, respectively. From the perspective of traffic planners, in this research, the integrated planning problem of multi-type lanes and intersections (IPPLI for short) in the mixed transportation network is suggested and tackled, where the route selection behavior and the cross-group externalities of heterogeneous travelers are considered according to the user equilibrium principle. It aims to minimize the overall travel cost by making decisions on the spatial layout of multi-type lanes and intersections in the network, the toll level of CAV/toll lanes, the number of exclusive approaches at novel signalized intersections, time intervals of the cycle and green signal for each phase at both conventional and novel signalized intersections. Then, the IPPLI is formulated as a mixed-integer nonlinear programming model based on the link-node modeling method without time-consuming path enumeration and memory-consuming path storage. As a mathematical problem with complementarity constraints, it is solved by an improved evolutionary algorithm-based approach, which consists of two modules cooperating with each other. After introducing the concept of the accessibility of HVs, a heuristic technique is proposed to accelerate algorithm convergence by continuously repairing unreasonable solutions. Finally, experiments are performed on two distinct networks to showcase the properties of the problem and assess the effectiveness of the proposed model. Experimental results show that the proposed model consistently performs outstandingly across a range of CAV penetration rates. Our model achieves maximum improvements of 25.71% and 4.84% in reducing travel costs compared to models that only plan multi-type lanes and multi-type intersections, respectively. Additionally, the improved evolutionary algorithm-based approach reduces the convergence time by 20.51% and 26.81% compared to the classical evolutionary algorithm and the genetic algorithm provided by MATLAB Global Optimization Toolbox.

中文翻译：

---

对具有混合 HV 和 CAV 的运输网络中多类型车道和交叉路口进行集成优化规划


在较长的过渡期内，城市交通基础设施预计将容纳两种不同类型的车辆。一种类型的车辆是人类驾驶车辆 （HV），另一种是互联和自动驾驶车辆 （CAV）。然而，在混合交通场景中，HV 和 CAV 之间的冲突严重阻碍了实施新兴 CAV 技术的效率提升效益。为了充分利用 CAV 在提高交通效率和网络性能方面的潜力，提出了多类型车道（即常规车道、专用 CAV 车道和 CAV/收费车道）和多类型交叉路口（即传统信号交叉路口、具有专用相位和专用方法的新型信号灯交叉路口，以及智能无信号交叉路口），以有效管理道路和交叉路口的异构交通流量， 分别。本文从交通规划者的角度出发，提出并解决了混合交通网络中多类型车道和交叉口（简称IPPLI）的综合规划问题，其中根据用户均衡原则考虑了路线选择行为和异质出行者的跨群体外部性。它旨在通过决定网络中多种类型车道和交叉路口的空间布局、CAV/收费车道的收费水平、新型信号交叉路口的独家通道数量、循环的时间间隔和传统和新型信号交叉路口每个阶段的绿色信号来最大限度地降低总体旅行成本。 然后，将 IPPLI 公式化为基于链路节点建模方法的混合整数非线性规划模型，无需耗时的路径枚举和耗内存的路径存储。作为一个具有互补性约束的数学问题，它通过一种改进的基于进化算法的方法来解决，该方法由两个相互配合的模块组成。在引入 HV 的可访问性概念后，提出了一种启发式技术，通过不断修复不合理的解来加速算法收敛。最后，在两个不同的网络上进行实验，以展示问题的性质并评估所提出模型的有效性。实验结果表明，所提出的模型在一系列 CAV 穿透率上始终表现出色。与仅规划多类型车道和多类型交叉路口的模型相比，我们的模型在降低出行成本方面分别实现了 25.71% 和 4.84% 的最大改进。此外，与经典进化算法和 MATLAB Global Optimization Toolbox 提供的遗传算法相比，改进的基于进化算法的方法将收敛时间缩短了 20.51% 和 26.81%。