Research on intelligent traffic light control system based on dynamic Bayesian reasoning

被引：7

作者：

Xiao Zhengxing ^{[1
]}

Jiang Qing ^{[2
]}

Nie Zhe ^{[1
]}

Wang Rujing ^{[2
]}

Zhang Zhengyong ^{[2
]}

Huang He ^{[2
]}

Sun Bingyu ^{[2
]}

Wang Liusan ^{[2
]}

Wei Yuanyuan ^{[2
]}

机构：

[1] Shenzhen Polytech, Sch Comp & Engn, Shenzhen 518055, Peoples R China

[2] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2020年 / 84卷

基金：

中国国家自然科学基金;

关键词：

Intelligent traffic light; Dynamic Bayesian network; Intelligent decision model; Dynamic Bayesian reasoning; NETWORKS;

D O I：

10.1016/j.compeleceng.2020.106635

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Intelligent traffic lights are an important part of intelligent transportation systems. In this paper, the Bayesian network theory is used to establish a traffic light independent intelligent decision model based on dynamic Bayesian network. According to the real-time dynamic information of traffic conditions, the proposed dynamic Bayesian network approximate reasoning algorithm is used to realize online reasoning and determine the best traffic light time. The algorithm combines the time window with the improved forward-backward algorithm. By adjusting the time window width of the algorithm, the evidence information can be used to maximize online reasoning. Compared with the existing time window based on interface algorithm, it's proved that the reasoning algorithm proposed is more efficient. The research results of this paper have important practical significance in solving the traffic congestion problem and reducing the waiting time of people at the intersection of traffic lights. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：13

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