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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.Abstract: Based on the theory of cloud control systems, an intelligent transportation cyber-physical cloud control system is designed due to the problems of complex objects, big data, high demand for transmission and calculation and poor real-time control ability in the modern intelligent transportation cyber-physical network. It includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on the big data of intelligent traffic flow, two intelligent learning methods, deep learning and extreme learning machine, are used to train and predict the traffic flow data on the servers of the cloud control management center. The short time traffic flow and the congestion of roads are predicted accurately. Then the real-time traffic flow control strategy is obtained by intelligent optimization scheduling algorithm in the cloud. The problem of traffic flow distribution in congested roads is solved and the dynamic performance of intelligent transportation control systems can be improved. The simulation results show the effectiveness of the proposed method.1) 本文责任编委 贺威
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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 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 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 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
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 
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