Reliability-based mixed traffic equilibrium problem under endogenous market penetration of connected autonomous vehicles and uncertainty in supply

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 penetration rates 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.