How Smart Traffic Systems Use AI and Sensors to Optimize Urban Flow

Smart traffic systems replace traditional time-based signal cycles with dynamic, sensor-driven control that responds to real-time vehicle presence and density. These systems use inputs from cameras, inductive loops, and lidar to continuously observe, analyze, and adjust signal timings at intersections. Decision-making logic often draws on techniques such as reinforcement learning, genetic algorithms, or heuristic rules to minimize wait times and maximize throughput. From a software engineering perspective, the core architecture resembles a distributed control loop where an intelligent agent issues commands to traffic light controllers based on predicted congestion. The article also presents a simplified pseudocode model illustrating how a single intersection controller might manage phase transitions and sensor data updates.