城市交通干线递阶模糊控制及其神经网络实现

利用大系统的分解-协调思想、模糊理论和神经网络技术来进行城市交通干线的实时协调控制.把交通干线作为一个大系统,子系统为干线上的各个交叉口,在此基础上,设计了一种城市交通干线的两级模糊协调控制算法并用BP神经网络实现.控制级在线调整各子系统的信号周期和绿信比;而协调级则根据测得的交通信息协调相邻子系统间的车辆数.控制目标是使干线交通畅通并使平均车辆延误时间尽可能小.最后进行了仿真研究,结果表明,该方法比车辆全感应式控制能有效地减小平均车辆延误.

Hierarchical Fuzzy Control for Urban Traffic Trunk Roads and Its Neural Network Implementation

This paper uses the principle of decomposition-coordination for large-scale systems, fuzzy theory and neural networks technique to solve the real time arterial coordinated control problem. The urban traffic trunk road is regarded as a large system, and the subsystems are the intersections in the trunk road. A neural network implemented two-level coordinated fuzzy control method is presented. The second layer adjusts on-line signal cycle and splits of every intersection. Based on the traffic volume data measured from every intersection the first layer coordinates the number vehicle between the adjacent intersections, the object of the controller is to make the traffic trunk road unblocked and the average vehicle delay time shorten. The simulation shows the proposed method has better performances than the vehicle actuated method.