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城市区域交通智能分散控制研究
引用本文:沈国江.城市区域交通智能分散控制研究[J].浙江大学学报(自然科学版 ),2006,40(4):585-589.
作者姓名:沈国江
作者单位:沈国江(浙江大学 工业控制技术国家重点实验室,浙江 杭州 310027)
基金项目:中国科学院资助项目;浙江省杭州市科技创新项目
摘    要:针对城市区域交通非线性、不确定性和模糊性特点,提出了一种新颖的实时智能分散控制策略.把整个城市区域交通作为一个大系统,区域中的各交叉口作为子系统,在每个交叉口设置一个独立的控制器,该控制器根据自己和相邻交叉口的交通流信息对交叉口的相序、相位切换、信号周期和绿信比进行动态优化.每个控制器有3个模块组成:相序优化模块、绿灯判断模块和相位切换模块.对每个控制模块设计了相应的模糊优化控制算法,并用改进的BP神经网络实现算法的模糊关系.控制目标是保持区域内各交叉口前的交通畅通和车辆延误最小.仿真研究表明,在交通流量较大和流量时变的环境下,智能分散控制方法比普通单交叉口车辆感应控制方法的控制效果更好,实用性更强.

关 键 词:控制理论与工程  城市区域  模糊控制  神经网络  分散控制
文章编号:1008-973X(2006)04-0585-05
收稿时间:2005-12-05
修稿时间:2005年12月5日

Study on intelligent decentralized control for urban region traffic
SHEN Guo-jiang.Study on intelligent decentralized control for urban region traffic[J].Journal of Zhejiang University(Engineering Science),2006,40(4):585-589.
Authors:SHEN Guo-jiang
Affiliation:National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Chirps
Abstract:A novel real-time intelligent decentralized control strategy applicable to the nonlinear,uncertain,fuzzy system of urban region traffic was presented.The whole urban region traffic was regarded as a large scale system and the subsystem was every single intersection in the region.Each intersection had its own traffic controller which managed the phase sequence,phase switch,cycle time and splits dynamically according to its own and its neighbor's traffic situations.The controller consisted of three modules that were the phase-sequence optimizing module,the green-phase judging module and the phase switching module.Fuzzy optimal control arithmetic was designed for each module,and an improved BP neural network was introduced to implement the fuzzy relation.The method aimed at making the intersections unblocked and making the average vehicle delay time shortest.The results of simulation show that this method has better performances in the cases of time-varying traffic patterns and heavy traffic conditions than the vehicle actuated method.This intelligent decentralized control strategy can be used for urban traffic control.
Keywords:control theory and engineering  urban regions fuzzy control  neural networks  decentralized control
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