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考虑多源不确定信息的路网交通拥堵状态辨识方法

黄大荣 柴彦冲 赵玲 孙国玺

黄大荣, 柴彦冲, 赵玲, 孙国玺. 考虑多源不确定信息的路网交通拥堵状态辨识方法. 自动化学报, 2018, 44(3): 533-544. doi: 10.16383/j.aas.2018.c160373
引用本文: 黄大荣, 柴彦冲, 赵玲, 孙国玺. 考虑多源不确定信息的路网交通拥堵状态辨识方法. 自动化学报, 2018, 44(3): 533-544. doi: 10.16383/j.aas.2018.c160373
HUANG Da-Rong, CHAI Yan-Chong, ZHAO Ling, SUN Guo-Xi. Traffic Congestion Status Identification Method for Road Network with Multi-source Uncertain Information. ACTA AUTOMATICA SINICA, 2018, 44(3): 533-544. doi: 10.16383/j.aas.2018.c160373
Citation: HUANG Da-Rong, CHAI Yan-Chong, ZHAO Ling, SUN Guo-Xi. Traffic Congestion Status Identification Method for Road Network with Multi-source Uncertain Information. ACTA AUTOMATICA SINICA, 2018, 44(3): 533-544. doi: 10.16383/j.aas.2018.c160373

考虑多源不确定信息的路网交通拥堵状态辨识方法

doi: 10.16383/j.aas.2018.c160373
基金项目: 

国家自然科学基金 61663008

广东省石化装备故障诊断重点实验室开放式基金 GDUPTKLAB201501

重庆市高等教育学会高等学校2015-2016年教改项目 CQGJ15010C

重庆市研究生教改重点项目 yjg152011

国家自然科学基金 61304104

国家自然科学基金 61573076

教育部留学归国人员科研启动基金 2015-49

重庆市高等学校优秀人才支持计划 2014-18

详细信息
    作者简介:

    黄大荣 重庆交通大学信息科学与工程学院教授.主要研究方向为故障诊断与容错控制, 交通信息与控制.E-mail:hcx1978@163.com

    赵玲 重庆交通大学信息科学与工程学院副教授.主要研究方向为信号处理.E-mail:zhaoling@cqjtu.edu.cn

    孙国玺 广东石油化工学院教授.2006年获得华南理工大学电路和系统专业博士学位.主要研究方向为故障诊断, 剩余寿命预测, 数据挖掘.E-mail:sguoxi@126.com

    通讯作者:

    柴彦冲 重庆交通大学信息科学与工程学院硕士研究生.主要研究方向为交通信息化, 大数据处理.本文通信作者.E-mail:chaiyanchong@163.com

Traffic Congestion Status Identification Method for Road Network with Multi-source Uncertain Information

Funds: 

National Natural Science Foundation of China 61663008

Opening Fund of Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis GDUPTKLAB201501

Chongqing Association of Higher Education 2015-2016 Research Project CQGJ15010C

Research Project for Graduate Education of Chongqing yjg152011

National Natural Science Foundation of China 61304104

National Natural Science Foundation of China 61573076

the Science Research Foundation for Returned Scholars, Ministry of Education of China 2015-49

Program for Excellent Talents of Chongqing Higher School 2014-18

More Information
    Author Bio:

    Professor at the College of Information Science and Engineering, Chongqing Jiaotong University. His research interest covers failure detection and fault-tolerent control, transportation information and control

    Associate professor at the College of Information Science and Engineering, Chongqing Jiaotong University. Her main research interest is signal processing

    Professor at Guangdong University of Petrochemical Technology. He received his Ph. D. degree from South China University of Technology in 2006. His research interest covers failure detection, residual service life prediction, and data mining

    Corresponding author: CHAI Yan-Chong Master student at the College of Information Science and Engineering, Chongqing Jiaotong University. His research interest covers transportation informatization and large dataset processing. Corresponding author of this paper
  • 摘要: 拥堵状态辨识是道路运行状态评估的重要内容,是交通系统流量调控和管理的重要参考指标.在智能交通系统(Intelligent transport system,ITS)普及化程度越来越高的后交通时代,如何实现海量数据下对多源不确定交通拥堵状态的辨识是非常重要的内容.首先,基于多元集对分析建立一种新的路网交通拥堵状态刻画模型;然后,通过改进证据理论中Dempster组合规则实现交通信息融合,并推导出当前交通拥堵状态的准确表达值;最后,在数值模拟的基础上,使用重庆市南岸区的交通检测数据进行仿真分析,结果表明本方法能准确直观地反映出实时交通拥堵状态,具有潜在的实际应用价值.
    1)  本文责任编委 侯忠生
  • 图  1  南岸区路网

    Fig.  1  Road network of Nanan district

    图  2  观测示意图

    Fig.  2  The observation sketch

    图  3  拥堵状态曲线

    Fig.  3  The curve of congestion state

    图  4  软件初始界面

    Fig.  4  The initial software interface

    图  5  轻度拥堵状态

    Fig.  5  Mild congestion state

    图  6  中度拥堵状态

    Fig.  6  Moderate congestion state

    图  7  重度拥堵状态

    Fig.  7  Severe congestion state

    图  8  区域路网拥堵曲线

    Fig.  8  The regional road network congestion curve

    图  9  区域路网拥堵对比曲线

    Fig.  9  The regional road network congestion correlation curve

    表  1  $m_1$ 与 $m_2$ 的融合过程

    Table  1  Fusion process of $m_1$ and $m_2$

    $\theta_1(0.2)$ $\theta_2(0.4)$ $\theta_3(0.2)$ $\theta_4(0.1)$ $\theta_5(0.1)$
    $\theta_1(0.1)$ 0.02 0.04 0.02 0.01 0.01
    $\theta_2(0.4)$ 0.08 0.16 0.08 0.04 0.04
    $\theta_3(0.3)$ 0.06 0.12 0.06 0.03 0.03
    $\theta_4(0.1)$ 0.02 0.04 0.02 0.01 0.01
    $\theta_5(0.1)$ 0.02 0.04 0.02 0.01 0.01
    下载: 导出CSV

    表  2  $m_{1, 2}$ 与 $m_3$ 的融合过程

    Table  2  Fusion process of $m_{1, 2}$ and $m_3$

    $\theta_1(0.08)$ $\theta_2(0.61)$ $\theta_3(0.23)$ $\theta_4(0.04)$ $\theta_5(0.04)$
    $\theta_1(0.1)$ 0.008 0.061 0.023 0.004 0.004
    $\theta_2(0.5)$ 0.040 0.305 0.115 0.020 0.020
    $\theta_3(0.2)$ 0.016 0.112 0.046 0.008 0.008
    $\theta_4(0.1)$ 0.008 0.061 0.023 0.004 0.004
    $\theta_5(0.1)$ 0.008 0.061 0.023 0.004 0.004
    下载: 导出CSV
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  • 收稿日期:  2016-05-05
  • 录用日期:  2016-11-17
  • 刊出日期:  2018-03-20

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