Effects of Smart Traffic Signal Control on Air Quality

TL;DR

This paper investigates how adaptive multi-agent deep reinforcement learning for traffic signals can reduce air pollution in urban networks, demonstrating promising environmental benefits.

Contribution

It introduces the MA2C algorithm, a multi-agent A2C variant with communication, and evaluates its effectiveness in real traffic networks to improve air quality.

Findings

MA2C significantly reduces pollutants in tested networks.

Communication among agents stabilizes learning in complex traffic systems.

Results show environmental benefits of intelligent traffic control.

Abstract

Adaptive traffic signal control (ATSC) in urban traffic networks poses a challenging task due to the complicated dynamics arising in traffic systems. In recent years, several approaches based on multi-agent deep reinforcement learning (MARL) have been studied experimentally. These approaches propose distributed techniques in which each signalized intersection is seen as an agent in a stochastic game whose purpose is to optimize the flow of vehicles in its vicinity. In this setting, the systems evolves towards an equilibrium among the agents that shows beneficial for the whole traffic network. A recently developed multi-agent variant of the well-established advantage actor-critic (A2C) algorithm, called MA2C (multi-agent A2C) exploits the promising idea of some communication among the agents. In this view,the agents share their strategies with other neighbor agents, thereby stabilizing the learning process even when the agents grow in number and variety. We experimented MA2C in two traffic networks located in Bologna (Italy) and found that its action translates into a significant decrease of the amount of pollutants released into the environment.