Machine Learning and Robotics in Urban Traffic Flow Optimization With Graph Neural Networks and Reinforcement Learning

Abstract

Increased congestion, inefficiency, and accidents in cities are major issues for urban traffic systems. However, rapid urbanization and increasing numbers of cars exacerbate problems that have created an environment too dynamic and sophisticated for traditional solutions like static traffic signals or road expansion. The chapter discusses the use of machine learning and robotics with graph neural networks and reinforcement learning for optimizing traffic flow. Traffic networks pose intricate relationships that GNNs model under the form of nodes and edges representing roads, intersections, and vehicles. RL allows for continuous real-time interaction through which autonomous agents learn optimal strategies; thus, better decision-making takes place in dynamic traffic conditions and the system can proactively adjust signal timings, reroute vehicles, and manage congestion. Integration of these technologies will indeed be transformative to traffic management; hence, more effective, flexible, safest transportation systems will be expected in the future.