Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.