Cooperative Control of Intersection Traffic Signals Based on Multi-Agent Reinforcement Learning for Carbon Dioxide Emission Reduction

Abnormal weather is occurring around the world, including the hottest weather in 174 years of observation records, the largest fire in Europe's observation records, and approximately twice the average annual rainfall recorded in one day. This abnormal climate is highly related to greenhouse gases, and efforts to reduce emissions are required in various fields. This study aims to reduce carbon dioxide emissions in the transportation sector, which accounts for a high proportion of emissions. A multi-agent reinforcement learning technique is used for adaptive traffic signal control, and especially a novel cooperative approach is introduced, when considering neighboring intersections. We consider not only the adjacent intersection's last reward as a Q-function but also its state and action as state. This method has the advantage of considering only vehicles from adjacent intersections that enter an intersection. The proposed method was evaluated on roads in Icheon City, and the results show that it reduces waiting time and carbon dioxide emissions.