自动化学报2019，Vol.45Issue(1)：132-142,11.DOI:10.16383/j.aas.c180370
智能交通信息物理融合云控制系统
Intelligent Transportation Cyber-physical Cloud Control Systems
摘要
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.关键词
智能交通云控制系统/深度学习/超限学习/信息物理融合系统Key words
Intelligent transportation cloud control systems/deep learning/extreme learning machine/cyber-physical systems引用本文复制引用
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[J].自动化学报,2019,45(1):132-142,11.基金项目
国家重点研发计划(2018YFB1003700) （2018YFB1003700）
国家自然科学基金(61836001,61803033) （61836001,61803033）
国家自然科学基金国际合作交流项目(61720106010) （61720106010）
国家自然科学基金创新研究群体基金(61621063) （61621063）
北京市自然科学基金(4161001,Z170039)资助 （4161001,Z170039）
