In this paper, we consider a novel reliability-based network equilibrium problem for mixed traffic flows of human-driven vehicles (HVs) and connected autonomous vehicles (CAVs) with endogenous CAV market penetration and stochastic link capacity degradations. Travelers’ perception errors on travel time and their risk-aversive behaviors on mode choice and path choice are incorporated in the model with a hierarchical choice structure. Due to the differences between HVs and CAVs, the perception errors and the safety margin reserved by risk-averse travelers are assumed to be related to the vehicle type. The path travel time distribution is derived by using the moment-matching method based on the assumption that link capacity follows lognormal distribution and link travel times are correlated. Then, the underlying problem is formulated as an equivalent variational inequality problem. A path-based algorithm embedded with the Monte Carlo simulation-based method is proposed to solve the model. Numerical experiments are conducted to illustrate the features of the model and the computational performance of the solution algorithm.

在本文中，我们考虑了一种新颖的基于可靠性的网络平衡问题，用于解决人类驾驶车辆 (HV) 和联网自动驾驶车辆 (CAV) 的混合交通流问题，并具有内生 CAV 市场渗透率和随机链路容量下降。旅行者对出行时间的感知误差以及他们对模式选择和路径选择的风险规避行为被纳入具有分层选择结构的模型中。由于HV和CAV之间的差异，风险厌恶出行者的感知误差和保留的安全裕度被认为与车辆类型有关。基于路段容量服从对数正态分布且路段行程时间相关的假设，采用矩匹配法推导出路径行程时间分布。然后，将根本问题表述为等价变分不等式问题。提出了一种嵌入基于蒙特卡罗模拟的方法的基于路径的算法来求解该模型。通过数值实验来说明模型的特点和求解算法的计算性能。