It is widely recognized that human-driven vehicles (HVs) and connected and autonomous vehicles (CAVs) are expected to coexist and share the urban traffic infrastructure in the transportation network for a long time. To fully utilizes CAVs’ potential to reduce congestion in the transitional period, this study proposes and addresses the intersection design and signal setting problem in the transportation network with mixed HVs and CAVs. Due to the difference in terms of communication technology and autonomous driving technology for HVs and CAVs, three types of intersections have been proposed to amplify the efficiency-improvement benefit from CAVs by separating CAVs from HVs in a temporal or local-spatial dimension: the conventional signalized intersection, the novel signalized intersection with a dedicated CAV phase and dedicated CAV approaches, and the intelligent signal-free intersection. The problem is to determine the spatial layout of different types of intersections in the transportation network, the cycle time, and green time duration for each phase of signalized intersections that minimize the total travel cost, in which the route choice behavior of heterogeneous travelers has been respected based on the user equilibrium principle. A mixed-integer nonlinear programming model is developed to formulate the proposed intersection design and signal setting problem based on the link-node modeling method, in which the path enumeration is avoided. Then, by employing various linearization techniques (e.g., disjunctive constraints, logarithmic transformation, piecewise linearization with logarithmic-sized binary variables and constraints, outer-approximation technique), the proposed model can be further transformed into a relaxed sub-problem in the form of mixed-integer linear programming. A globally optimal solution algorithm embedding with solving a sequence of relaxed sub-problems and nonlinear mixed complementarity problems is proposed to converge to a global optimum. The results of numerical experiments illustrate that the proposed methodology can significantly improve the performance of the whole network. Moreover, it consistently outperforms the optimization model considering only conventional signalized intersections under various CAV market penetration rates.

人们普遍认为，人类驾驶车辆 (HV) 和联网自动驾驶车辆 (CAV) 有望长期共存并共享交通网络中的城市交通基础设施。为了充分利用 CAV 在过渡时期减少拥堵的潜力，本研究提出并解决了混合 HV 和 CAV 的交通网络中的交叉路口设计和信号设置问题。由于 HV 和 CAV 在通信技术和自动驾驶技术方面的差异，已经提出了三种类型的交叉路口，通过在时间或局部空间维度上将 CAV 与 HV 分开来放大 CAV 的效率改进效益：信号交叉口，具有专用 CAV 阶段和专用 CAV 方​​法的新型信号交叉口，以及智能无信号路口。问题是确定交通网络中不同类型交叉口的空间布局，信号交叉口各阶段的周期时间和绿灯时长，使总出行成本最小，其中异质出行者的路径选择行为已经被尊重基于用户均衡原则。基于链接节点建模方法，开发了混合整数非线性规划模型来制定所提出的交叉口设计和信号设置问题，其中避免了路径枚举。然后，通过采用各种线性化技术（例如，析取约束、对数变换、具有对数大小的二进制变量和约束的分段线性化、外部近似技术），所提出的模型可以进一步转化为混合整数线性规划形式的松弛子问题。提出了一种嵌入求解一系列松弛子问题和非线性混合互补问题的全局最优解算法以收敛到全局最优。数值实验的结果表明，所提出的方法可以显着提高整个网络的性能。此外，在各种 CAV 市场渗透率下，它始终优于仅考虑传统信号交叉口的优化模型。提出了一种嵌入求解一系列松弛子问题和非线性混合互补问题的全局最优解算法以收敛到全局最优。数值实验的结果表明，所提出的方法可以显着提高整个网络的性能。此外，在各种 CAV 市场渗透率下，它始终优于仅考虑传统信号交叉口的优化模型。提出了一种嵌入求解一系列松弛子问题和非线性混合互补问题的全局最优解算法以收敛到全局最优。数值实验的结果表明，所提出的方法可以显着提高整个网络的性能。此外，在各种 CAV 市场渗透率下，它始终优于仅考虑传统信号交叉口的优化模型。