AI-Based Adaptive Traffic Management

Traffic congestion is one of the biggest and most important issues facing modern city dwellers, necessitating effective and efficient solutions. AI-powered traffic light optimization has grown in popularity. The available literature indicates that intelligent approaches outperform traditional management techniques. The amount of cars on each intersection road determines the signal timings, which are adjusted to reduce traffic at the intersection. The recommended topology may significantly reduce traffic jams and wait times at the intersection. A lot of benefits can arise from an efficient traffic control system, such as less traffic on the roads, more economic efficiency, and enhancements in pedestrian safety and air quality. This paper discusses the several phases of the process, including data collecting, data analysis, and simulation development. At the conclusion of the process, a Python simulation model is created to test the topology.