Models, algorithms, and equilibrium conditions for the simulation of autonomous vehicles in exclusive and mixed traffic

This paper proposes models and algorithms for the estimation of traffic flows generated by the centralised management of autonomous vehicles under exclusive and mixed traffic conditions. It is assumed that the autonomous vehicle manager wants to obtain the system optimum that minimises the total travel time on the network, of autonomous vehicles in the case of exclusive traffic, and of all flows in the case of mixed traffic. In the case of mixed traffic, the drivers of the human-guided vehicles choose their routes from a user-optimal perspective; in this case, a fixedpoint problem has to be solved, which represents a state of equilibrium between the flows of human-guided vehicles, the flows of autonomous vehicles, which follow paths aimed at the system optimum, and the link costs. The proposed models and algorithms are tested on a small network with a single origin-destination pair, and on the Sioux Falls network, with different levels of congestion and different percentages of autonomous vehicles. The results show the validity of the proposed methods and the impact of autonomous vehicles on network performance. The proposed static/equilibrium approach is necessary to evaluate transport planning, design or policy interventions that include the presence of autonomous vehicles in traffic flows.