The Traffic Light Control Strategy at Intersections in Intelligent Transportation Systems

This thesis explores strategies for controlling traffic signals at intersections within the context of ITS., emphasizing the role of DRL in optimizing traffic flow. In recent years, urbanization and the rapid increase in vehicle numbers in China have exacerbated traffic congestion, significantly hindering urban development. This study explores innovative approaches to alleviating traffic congestion, focusing on smart traffic signal systems that adjust according to real-time traffic conditions. The research reviews fundamental concepts in traffic signal control, including traffic flow, signal phases, and signal cycles, and investigates how DRL can dynamically adjust traffic light cycles to optimize intersection performance. The findings suggest that DRL provides an effective method for managing complex and unpredictable traffic environments, as it enables systems to self-learn and continuously refine their strategies based on environmental changes. The adoption of this technology holds the potential to greatly optimize traffic flow, alleviate congestion, and boost the performance of urban transportation systems. The study concludes that signal control strategies based on DRL present a viable approach to tackling the issues associated with growing traffic and urban congestion.