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Fuzzy neural network controllers and simulations for urban region traffic

机译:城市区域交通的模糊神经网络控制器与仿真

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This paper uses the fuzzy neural networks (FNN) theory to solve the real time region traffic distributed control problem. The region traffic is regarded as a large scale system and the subsystems are the intersections in the region. Each intersection has its own traffic controller which manages the green phase and the phase length dynamically according to its own and its neighbor's traffic situations. The object of the controller is to make the lane unblocked and the average vehicle delay time shortest in the front of each intersection. The proposed method is compared with the vehicle actuated method which is one of the type conventional methods. The simulation show good performances in the cases of time-varying traffic patterns and heavy traffic situations.
机译:本文运用模糊神经网络(FNN)理论解决了实时区域交通分布控制问题。区域交通被视为大型系统,子系统是区域中的交叉路口。每个十字路口都有自己的交通控制器,该控制器根据自己和邻居的交通状况动态管理绿相和相长。控制器的目的是使车道畅通无阻,并使每个交叉口前的平均车辆延迟时间最短。将该方法与传统方法之一的车辆驱动方法进行了比较。在时变交通模式和繁忙交通情况下,仿真显示了良好的性能。

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