Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

TL;DR

This paper introduces EcoLight, a reinforcement learning reward scheme that optimizes traffic signal control to reduce CO2 emissions while maintaining efficient traffic flow, evaluated across various algorithms and scenarios.

Contribution

It presents EcoLight, a novel reward shaping method for reinforcement learning that specifically targets CO2 emission reduction in traffic signal control.

Findings

EcoLight effectively reduces CO2 emissions in multiple traffic scenarios.

Reinforcement learning algorithms with EcoLight outperform traditional methods in environmental metrics.

The approach maintains competitive travel times despite emission reductions.

Abstract

Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion. Increased levels of air pollution and extended commute times caused by traffic bottlenecks make intersection traffic signal controllers a crucial component of modern transportation infrastructure. Despite several adaptive traffic signal controllers in literature, limited research has been conducted on their comparative performance. Furthermore, despite carbon dioxide (CO2) emissions' significance as a global issue, the literature has paid limited attention to this area. In this report, we propose EcoLight, a reward shaping scheme for reinforcement learning algorithms that not only reduces CO2 emissions but also achieves competitive results in metrics such as travel time. We compare the performance of tabular Q-Learning, DQN, SARSA, and A2C algorithms using metrics such as travel time, CO2 emissions, waiting time, and stopped time. Our evaluation considers multiple scenarios that encompass a range of road users (trucks, buses, cars) with varying pollution levels.