Reinforcement Learning-Based Control of Signalized Intersections Having Platoons

Smart transportation cities are based on intelligent systems and data sharing, whereas human drivers generally have limited capabilities and imperfect traffic observations. The perception of Connected and Autonomous Vehicle (CAV) utilizes data sharing through Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communications to improve driving behaviors and reduce traffic delays and fuel consumption. This paper proposes a Double Agent (DA) intelligent traffic signal module based on the Reinforcement Learning (RL) method, where the first agent, the Velocity Agent (VA) aims to minimize the fuel consumption by controlling the speed of platoons and single CAVs crossing a signalized intersection, while the second agent, the Signal Agent (SA) proceeds to efficiently reduce traffic delays through signal sequencing and phasing. Several simulation studies have been conducted for a signalized intersection with different traffic flows and the performance of the single-agent with only VA, DA with both VA and SA, and Intelligent Driver Model (IDM) are compared. It is shown that the proposed DA solution improves the average delay by 47.3% and the fuel efficiency by 13.6% compared to the Intelligent Driver Model (IDM).

Automated summary

Smart transportation cities rely on intelligent systems and data sharing to enhance driving behavior and reduce traffic delays and fuel consumption. This paper proposes a Double Agent intelligent traffic signal module based on Reinforcement Learning, which improves driving efficiency and fuel efficiency by controlling speed and signal sequencing. Simulation studies show that this module outperforms traditional models in reducing delays and improving fuel efficiency.