区分交通流模式的混合服务路口信号控制策略
作者:
作者单位:

(1. 云南大学信息学院,昆明650091;2. 六盘水师范学院物理与电气工程学院,贵州六盘水553004)

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E-mail: gz_627@sina.com.

中图分类号:

TP273

基金项目:

国家自然科学基金项目(61761045);云南省应用基础研究计划项目(2017FB100);六盘水师范学院硕士培育点项目(LPSSYSSDPY201704);六盘水师范学院重点专业项目(LSZDZY2018-03).


Signal control strategies of mixed service intersections to discriminate traffic flow patterns
Author:
Affiliation:

(1. School of Information Science and Technology,Yunnan University,Kunming650091,China;2. School of Physics and Electrical Engineering,Liupanshui Normal University,Liupanshui553004,China)

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    摘要:

    相对于固定配时,基于交通流的动态变化特征的信号配时算法具有更好的道路状态适应性.鉴于此,提出一种基于交通流识别的自适应控制策略,首先利用自组织映射网络(SOM)神经网络对历史交通流状态聚类,结合路口时间段与路段环境特征分析实现交通流模式划分;在此基础上,引入概率神经网络(PNN)对该路口的交通流模式进行训练学习;最后针对不同状态类型交通流量,动态选取门限服务轮询信号配时和韦伯斯特信号配时策略计算信号灯配时周期,实现控制策略与交通流动态变化特征的匹配.仿真实验结果表明,区分交通流模式的混合服务路口信号控制方法对车流的随机变化具有更好的适应性.

    Abstract:

    Compared with the fixed timing, the signal timing algorithm based on the dynamic change characteristics of traffic flow has better road state adaptability. Therefore, this paper proposes an adaptive control strategy based on traffic flow identification, firstly the self-organizing mapping network(SOM) neural network is used to cluster the historical traffic flow state, and the environment feature analysis of the intersection time segment and the road segment is combined to realize the traffic flow mode division. On this basis, the probabilistic neural network (PNN) is introduced to train the traffic flow pattern of the intersection. Finally, according to the traffic flow of different state types, the threshold service polling signal timing and Webster signal timing strategy is dynamically selected to calculate the signal lamp timing period, and the matching between the control strategy and the traffic flow dynamic change characteristics is realized. The simulation results show that the mixed service intersection signal control method, which distinguishes the traffic flow mode, has better adaptability to the random variation of traffic flow.

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童林,官铮,杨文韬,等.区分交通流模式的混合服务路口信号控制策略[J].控制与决策,2021,36(6):1509-1515

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  • 在线发布日期: 2021-05-10
  • 出版日期: 2021-06-20