考虑个体车辆先验数据的交通信号 模糊控制研究

This study proposes a traffic signal fuzzy control method based on vehicle travel time and route prediction to fully utilize the historical travel information of individual vehicles for signal control optimization. First, the travel time of individual vehicles from upstream to the target intersection was predicted using the Bayesian optimized support vector machine model combined with individual vehicles and external environmental characteristics. Second, a Bayesian vehicle route prediction model was established based on the Markov chain, predicting vehicle travel routes and analyzing vehicle turning behaviors according to real-time license plate recognition results. Subsequently, based on the time and turning of individual vehicles arriving at the intersection, the predicted arrival rate in each direction at each intersection entrance was aggregated, and the fuzzy controller outputted the green light delay based on the predicted arrival rate, determining the next execution phase. Finally, the traffic signal fuzzy control method based on vehicle travel time and route prediction was verified, and a benefit analysis was conducted using the SUMO simulation platform. In the analysis, the proposed method was compared with the traditional fixed arrival rate fuzzy control method using case studies conducted during both off-peak and peak periods. The research results show that the new method proposed in this study, compared with the traditional fuzzy control method, reduces the average vehicle delay by 18. 75% during off-peak times and 16. 11% during peak times, whereas the flexible phase sequence control method improves the utilization efficiency of the green light time.