工业工程

工业工程 ›› 2018, Vol. 21 ›› Issue (6): 40-45.doi: 10.3969/j.issn.1007-7375.2018.06.006

• 实践与应用 • 上一篇    下一篇

考虑多交叉口相互影响的干道相位差仿真优化研究

曹涛涛1,2, 蒋阳升1,2, 赵斌1,2, 姚志洪1,2, 谭宇1   

  1. 西南交通大学 1.交通运输与物流学院;
    2. 综合交通大数据应用技术国家工程实验室, 四川 成都 610031
  • 收稿日期:2018-06-20 出版日期:2018-12-30 发布日期:2018-12-29
  • 通讯作者: 蒋阳升(1976-)，男，湖南省人，教授，博士生导师，主要研究方向为交通系统优化与规划设计、智能公共交通系统、智能交通控制等。Email：jiangyangsheng@swtju.cn E-mail:jiangyangsheng@swtju.cn
  • 作者简介:曹涛涛(1991-)，男，陕西省人，硕士研究生，主要研究方向为智能交通控制
  • 基金资助:
    国家自然科学基金资助项目（51578465，71771190）；西南交通大学优秀博士学位论文培育资助项目（D-YB201708）

An Offset Simulation Optimization of Arterial Street Considering the Interaction of Multiple Intersections

CAO Taotao1,2, JIANG Yangsheng1,2, ZHAO Bin1,2, YAO Zhihong1,2, TAN Yu1   

  1. 1. School of Transportation and Logistics;
    2. National Engineering Laboratory of Application Technology of Integrated Transportation Big Data, Southwest Jiaotong University, Chengdu 610031, China
  • Received:2018-06-20 Online:2018-12-30 Published:2018-12-29

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摘要： 为解决传统相位差优化方法以相邻交叉口为基础，忽略了连续交叉口对应的多个相位差之间的内在相关性的问题，以连续交叉口的多个相位差为研究对象，建立干道车辆延误与相位差关系的神经网络模型，采用遗传算法求解。首先，根据调查数据搭建仿环境获取不同相位差对应的干道延误数据，基于此采用神经网络拟合干道车辆延误与相位差之间的关系。然后通过遗传算法寻找神经网络中的最优延误对应的各个交叉口的相位差值。最后，将本文优化结果与Synchro进行比较分析。结果表明，本文模型能够有效改进相位差配时方案，方案性能提升了22.27%。

关键词: 交通工程, 相位差, 神经网络, 遗传算法, 协调控制

Abstract: The traditional offset optimization method is based on adjacent intersections while it ignores the internal correlation of offset among multiple continuous intersections. In order to solve this problem, several offsets between multiple continuous intersection are taken as research objects, and through neural network an optimization model is established to describe the relationship between offset and arterial vehicle delay, and a genetic algorithm is used to gain the optimal offset scheme. Firstly, according to the survey data the simulation environment is built for different vehicle delay data corresponding to multiple offsets, and based on this, a neural network is used to fit the relationship between offset and vehicle delay. Secondly, to get the optimal offset scheme based on the neural network, a genetic algorithm is introduced. Finally, a simulation experiment is carried out to prove the model efficiency, and the model is compared with Synchro. The result shows that the proposed model can effectively improve the signal scheme, and the arterial vehicle delay can be decreased by 22.27%.

Key words: traffic engineering, offset, neural network, genetic algorithm, coordinated control

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