FPGA-Based Adaptive Traffic Light Control System Using Neural Networks

This paper presents a novel FPGA-based adaptive traffic-light control system. The proposed system employs a neural network approach to optimize traffic flow. By collecting real-time data on vehicle and pedestrian passage times, the system dynamically adjusts traffic signal timings to enhance traffic flow efficiency. This research utilizes a neural network model to learn and adapt to changing traffic conditions, providing a more responsive and efficient traffic management solution.