Adaptive Intelligent Sensing Control Method for Traffic Lights under Real-Time Vehicle Conditions Based on Logic Rules

Compared with traditional adaptive algorithms, the complex and changing operating environment of intelligent networks demands high real-time accuracy in data transmission, necessitating an accurate and adaptive traffic light control strategy.Machine learning (ML) techniques can predict traffic conditions based on historical data and real-time information.However, some scholars have mentioned that ML techniques are still deficient in real-time response and in coping with random traffic accidents.Traditional Reinforcement Learning (RL) requires repeated trial and error operations.Applying traditional RL techniques to the optimal control of traffic lights may lead to more serious traffic congestion in some cases.Therefore, this paper combines perceptual control with real-time adaptive control methods to provide a rule-based reasoning method for adaptive intelligent perception and precise control of traffic signals under real-time smart grid-connected hybrid vehicle conditions.IIoT (Industrial Internet of Things) devices are utilized to monitor the parking queue length, pedestrian flow, vehicle flow, and traffic flow in each lane in real time to dynamically adjust the green light duration.By adjusting the light priority according to real-time vehicle and road conditions, this method solves the problem of wasting green lights when random accidents occur in a certain lane, optimizes traffic light settings, achieves real-time precise control of lane flow, and improves the adaptability and precision of traffic lights.