Signal Timing Optimization with Connected Vehicle Technology: Platooning to Improve Computational Efficiency

Abstract

This paper develops a real-time traffic signal optimization algorithm in the presence of connected and autonomous vehicles (CAVs). The proposed algorithm leverages information from connected vehicles (CVs) arriving at an intersection to identify naturally occurring platoons that consist of both CVs and non-CVs. Signal timings are then selected to optimize the sequence at which these platoons are allowed to discharge through the intersection to minimize total vehicle delay. Longitudinal trajectory guidance that explicitly accounts for vehicle acceleration and deceleration behavior is provided to the lead autonomous vehicle (AV) in any platoon to minimize the total number of stopping maneuvers performed by all vehicles. Simulation tests reveal that the proposed platoon-based algorithm provides superior computational savings (over 95%) compared with a previously developed algorithm that focuses on optimizing departure sequences of individual vehicles, with negligible changes in operational performance. The computational savings allow the platoon-based algorithm to accommodate intersections with four multi-lane approaches and left turns, whereas large computational costs limited the previous vehicle-based algorithm to only two single-lane approaches without conflicting left turns. Additional simulation tests of the platoon-based algorithm on these more realistic intersection configurations show that intersection performance increases as the penetration rate of CAVs in the vehicle fleet increases. However, the marginal benefits decrease rapidly after the fleet is composed of 40% CAVs.