Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions
This addresses traffic congestion and environmental pollution for urban transportation systems, but it is incremental as it builds on existing reinforcement learning methods.
The paper tackled sub-optimal traffic signal control by proposing EcoLight, a reward shaping scheme for reinforcement learning algorithms, which reduced CO2 emissions by 15% and achieved competitive travel times in simulations.
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.