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 toward 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.

Highlights

  • The impact of air pollution on human health, whether due to vehicular traffic or from industrial sources, has been proven to be largely detrimental

  • (Section 4), we evaluate how the MA2C action translates in terms of pollutants released in the environment

  • We evaluate how MA2C performance translates in terms of emissions

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Summary

Introduction

The impact of air pollution on human health, whether due to vehicular traffic or from industrial sources, has been proven to be largely detrimental. According to WHO, the World Health Organization, in recent times (2016) there have been worldwide 4.2 million premature deaths due to air pollution (WHO, 2021). This mortality is due to exposure to small particulate matter of 2.5 microns or less in diameter (PM2.5), which cause cardiovascular and respiratory disease, and cancers. In order to avoid congestion and traffic jams, various artificial-intelligence based algorithms have been proposed. These algorithms are able to deal with the problem of managing traffic signal control to favor a smooth vehicle flow. Established approaches include fuzzy logic (Gokulan and Srinivasan, 2010), swarm intelligence (Teodorovi, 2008), and reinforcement learning (Sutton and Barto, 1998)

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