On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles
For transportation network operators, this work demonstrates the potential of optimally controlled CAVs to significantly enhance network performance, though the analysis is limited to simulation and lacks concrete numerical results.
This paper applies a decentralized optimal control framework for connected and automated vehicles (CAVs) in a transportation network and shows that introducing CAVs yields radically improved roadway capacity and network performance compared to human-driven vehicles.
The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. Several research efforts reported in the literature to date have proposed decentralized control algorithms to coordinate CAVs in various traffic scenarios, e.g., highway on-ramps, intersections, and roundabouts. However, the impact of optimally coordinating CAVs on the performance of a transportation network has not been thoroughly analyzed yet. In this paper, we apply a decentralized optimal control framework in a transportation network and compare its performance to a baseline scenario consisting of human-driven vehicles. We show that introducing of CAVs yields radically improved roadway capacity and network performance.