Neural network-based model for traffic light control

Traffic light regulation is a critical aspect of modern urban infrastructure. Traditional traffic light control systems rely on operators or predefined rules. We developed an intelligent system capable of independently and automatically managing traffic lights at an intersection. We modeled an urban traffic light system at a quad intersection and proposed their modes defined by sets of active and inactive traffic lights, ensuring the accident-free passage of vehicles. This reduces the traffic light control problem to a classification problem. We developed a neural network model for the traffic light system, which uses the number of vehicles in each direction as input and selects a traffic light mode to match the current traffic situation. We conducted experimental studies to determine the optimal model configuration. Out simulation experiments confirmed the feasibility of this approach and demonstrated the high efficiency of the proposed model.