LAUSR

The transportation industry is one of the main contributors to global warming since it is responsible for a quarter of greenhouse gas emissions. Due to society’s crucial dependence on fossil fuels and the rapid increase in mobility demands, the reduction of global vehicle emissions evolved into a significant challenge. In the urban transportation areas, signalized intersections can be considered the main bottlenecks in mitigating congestion and, therefore, vehicle emission. Our research focuses on the Traffic Signal Control problem since the efficient control of these intersections can significantly impact the productive hours and, through emission, the health of the citizens along with the depressing challenge of climate change. The Traffic Signal Control problem is well-studied and solved via several different techniques. However, most recently, Single and Multi-Agent Reinforcement Learning methods have arisen thanks to their performance and real-time applicability. Although rewarding schemes, which are the most crucial aspects of this method, do not seem to evolve at the same pace as the utilized techniques. In this paper, we propose a novel rewarding concept to compare its performance with the most common rewarding strategies in the literature. The results indicate that our approach outperforms its contenders from the literature in both classic and sustainability measures.