Optimizing Road Traffic Through Reinforcement Learning

In the article, modern approaches to the development of Intelligent Transportation Systems (ITS) aimed at optimizing urban traffic are analyzed. Special attention is paid to model-free reinforcement learning algorithms (Q-Learning and Deep Q-Learning) used for controlling traffic lights in dynamic road traffic conditions. Simulation results in the SUMO environment have proven that implementing such algorithms significantly reduces intersection queues and increases the capacity of the transportation network. In particular, the use of Deep Q-Learning enables more efficient processing of large volumes of data, including information from IoT sensors and V2X technologies, fostering realistic adaptation to changing traffic conditions. The prospects for further research involve enhancing deep neural networks and multi-agent methods, which will improve traffic management outcomes in urban environments and lay the foundation for creating ‘smart’ transportation infrastructures.