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智能交通信息物理融合云控制系统

夏元清 闫策 王笑京 宋向辉

夏元清, 闫策, 王笑京, 宋向辉. 智能交通信息物理融合云控制系统. 自动化学报, 2019, 45(1): 132-142. doi: 10.16383/j.aas.c180370
引用本文: 夏元清, 闫策, 王笑京, 宋向辉. 智能交通信息物理融合云控制系统. 自动化学报, 2019, 45(1): 132-142. doi: 10.16383/j.aas.c180370
XIA Yuan-Qing, YAN Ce, WANG Xiao-Jing, SONG Xiang-Hui. Intelligent Transportation Cyber-physical Cloud Control Systems. ACTA AUTOMATICA SINICA, 2019, 45(1): 132-142. doi: 10.16383/j.aas.c180370
Citation: XIA Yuan-Qing, YAN Ce, WANG Xiao-Jing, SONG Xiang-Hui. Intelligent Transportation Cyber-physical Cloud Control Systems. ACTA AUTOMATICA SINICA, 2019, 45(1): 132-142. doi: 10.16383/j.aas.c180370

智能交通信息物理融合云控制系统

doi: 10.16383/j.aas.c180370
基金项目: 

国家自然科学基金创新研究群体基金 61621063

国家自然科学基金 61803033

北京市自然科学基金 Z170039

北京市自然科学基金 4161001

国家重点研发计划 2018YFB1003700

国家自然科学基金 61836001

国家自然科学基金国际合作交流项目 61720106010

详细信息
    作者简介:

    闫策 北京理工大学自动化学院博士研究生.主要研究方向为云控制, 云工作流最优化调度, 智能交通, 执行器饱和控制, Delta算子, 有限频域.E-mail:yancemc@163.com

    王笑京 交通运输部公路科学研究院总工程师, 研究员.主要研究方向为智能交通系统, 交通信息与控制工程科学和技术.E-mail:xj.wang@rioh.cn

    宋向辉 交通运输部公路科学研究院研究员.主要研究方向为智能交通系统, 交通信息与控制工程科学和技术.E-mail:sxh@itsc.cn

    通讯作者:

    夏元清 北京理工大学自动化学院教授.主要研究方向为云控制, 云数据中心优化调度管理, 智能交通, 模型预测控制, 自抗扰控制, 飞行器控制和空天地一体化网络协同控制.本文通信作者.E-mail:xia_yuanqing@bit.edu.cn

Intelligent Transportation Cyber-physical Cloud Control Systems

Funds: 

Foundation for Innovative Research Groups of the National Natural Science Foundation of China 61621063

National Natural Science Foundation 61803033

 Z170039

 4161001

Supported by National Key Research and Development Program of China 2018YFB1003700

National Natural Science Foundation 61836001

National Natural Science Foundation Projects of International Cooperation and Exchanges 61720106010

More Information
    Author Bio:

    Ph. D. candidate at Beijing Institute of Technology. His research interest covers cloud control, cloud workflow optimization scheduling, intelligent transportation, actuator saturation, delta operator and finite frequency

    Research fellow and chief engineer at Research Institute of Highway Ministry of Transport. His research interest covers intelligent transportation system, science and technology of traffic information and control engineering

    Research fellow at Research Institute of Highway Ministry of Transport. Her research interest covers intelligent transportation system, science and technology of traffic information and control engineering

    Corresponding author: XIA Yuan-Qing Professor at the School of Automation, Beijing Institute of Technology. His research interest covers cloud control, cloud data center optimization scheduling and management, intelligent transportation, model predictive control, active disturbance rejection control, flight control and networked cooperative control for integration of space, air and earth. Corresponding author of this paper
  • 摘要: 针对现代智能交通信息物理融合路网建设中的对象种类复杂、采集数据量大、传输及计算需求高以及实时调度控制能力弱等问题,基于云控制系统理论,以现代智能交通控制网络为研究对象,设计了智能交通信息物理融合云控制系统方案,包括智能交通边缘控制技术和智能交通网络虚拟化技术.基于智能交通流大数据,在云控制管理中心服务器上利用深度学习和超限学习机等智能学习方法对采集的交通流数据进行训练预测计算,能够预测城市道路的短时交通流和拥堵状况.进一步在云端利用智能优化调度算法得到实时的交通流调控策略,用于解决拥堵路段交通流分配难题,提高智能交通控制系统动态运行性能.仿真结果表明了本文方法的有效性.
    1)  本文责任编委 贺威
  • 图  1  智能交通云控制系统示意图

    Fig.  1  Schematic diagram of intelligent transportation cloud control systems

    图  2  面向交通需求的交通云端协同控制

    Fig.  2  Cloud coordination control for traffic demand

    图  3  智能交通底层边缘控制

    Fig.  3  Intelligent transportation bottom edge control

    图  4  智能交通云控制网络虚拟化架构

    Fig.  4  Intelligent transportation cloud control network virtualization architecture

    图  5  DBN-SVR模型网络结构

    Fig.  5  Network structure of DBN-SVR model

    图  6  基于反向传播的双端超限学习机算法流程图

    Fig.  6  Algorithm flow chart of back propagation bilateral extreme learning machine

    图  7  智能交通云控制系统预测调度示意图

    Fig.  7  Schematic diagram of prediction scheduling for intelligent transportation cloud control systems

    图  8  DBN-SVR预测交通流与实际交通流的对比

    Fig.  8  Comparison of DBN-SVR prediction traffic flow and actual traffic flow

    图  9  DBN-SVR的预测误差

    Fig.  9  Predicted error with DBN-SVR

    图  10  BP-BELM预测交通流与实际交通流的对比

    Fig.  10  Comparison of BP-BELM prediction traffic flow and actual traffic flow

    图  11  BP-BELM的预测误差

    Fig.  11  Predicted error with BP-BELM

    图  12  交通拥堵仿真结果

    Fig.  12  Simulation result of traffic jams

    图  13  交通流增量分配后仿真结果

    Fig.  13  Simulation result after incremental traffic flow assignment

    表  1  三种预测模型的性能比较

    Table  1  Performance comparison of three prediction models

    模型 MSE MAPE (%)
    DBN-SVR 0.05999 1.68051
    BP-BELM 0.34084 10.7250
    LSTM 0.37157 105.6117
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-06-01
  • 录用日期:  2018-08-27
  • 刊出日期:  2019-01-20

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