Design of Traffic Light Control System Based on Adaptive Fuzzy Neural Network
In response to the growing number of motor vehicles in Chinese cities, traditional traffic signal control systems, which rely on fixed-time scheduling, are becoming less effective. To address this challenge, this paper introduces fuzzy control technology into traffic signal management, enabling adaptive and intelligent control of traffic lights. By integrating global optimization techniques, the system enhances the efficiency and responsiveness of urban traffic control, making it more dynamic and user-friendly. The system is structured into three layers: the management layer, the optimized scheduling layer, and the data acquisition layer. The management layer serves as the central command unit, responsible for macro-level decision-making during emergencies such as traffic congestion or accidents. The data acquisition layer collects real-time vehicle flow information from detectors at intersections and transmits it to the control center via a communication network. The control center then uses an expert system and neural networks to analyze the data and generate optimized scheduling instructions. These instructions are sent back to each intersection, where local fuzzy control strategies adjust the traffic signals based on current conditions, ensuring smoother traffic flow and better safety. When traffic demand is low, the signal cycle time (T) should be shorter, but not less than 15 seconds per phase (P) to ensure that the green light duration (tgi) is sufficient for vehicles to pass through safely. Conversely, when traffic volume is high, the cycle time can extend up to 120 seconds, but exceeding 60 seconds of red light may cause driver frustration. In extreme cases, the system operates at the minimum or maximum cycle times, though this can lead to traffic jams. Based on expert experience, a fuzzy control algorithm was developed, as shown in Figure 1, to dynamically adjust the signal timing according to traffic conditions. Figure 1: Fuzzy Control Algorithm for a Single Intersection Traffic demand is typically measured using two methods: one involves counting the number of vehicles stopped before the stop line between two detectors placed 80–100 meters apart. Another method uses occupancy rate, which reflects how much of the road is occupied by vehicles. If the occupancy rate is above a certain threshold, it indicates high demand; otherwise, traffic volume is used instead. These metrics help determine the appropriate signal timing. Fuzzy tables, as shown in Tables 1, 2, and 3, guide the system in adjusting the green light duration based on queue length and traffic conditions. Table 1: Vehicle Queue Length Change Fuzzy Table Table 2: i-Phase Green Light Time Change Fuzzy Table Table 3: Fuzzy Control Rule Table While individual intersections are managed locally, their operations are guided by global scheduling. This ensures that local optimizations align with overall traffic goals. Two main tasks underpin global optimization: collecting current and historical traffic data, and predicting future traffic patterns. This continuous loop allows the system to adapt in real time. Current technologies, such as adaptive surface control systems, enable efficient data collection through advanced interfaces. However, the challenge remains in effectively coordinating signals to prevent congestion and improve network efficiency. Neural networks offer powerful tools for modeling complex, nonlinear systems. Their ability to process large datasets and learn from past trends makes them ideal for traffic prediction. In urban settings, where traffic patterns tend to be stable over short periods, high-order neural networks can accurately forecast inflow and outflow, without needing to separately account for factors like road capacity or signal timing. This simplifies the model while maintaining accuracy, leading to smarter, more responsive traffic control solutions.
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