Traffic Control System Based on 5G Communication Network

被引:0
作者
Li, Aijuan [1 ]
Yuan, Wenchang [1 ]
Huang, Xin [1 ]
Qiu, Xuyun [1 ]
Ban, Xiaodong [1 ]
Zhang, Yuxing [2 ]
机构
[1] Shandong Jiaotong Univ, Sch Automot Engn, Jinan, Shandong, Peoples R China
[2] Qingdao Yinggu Educ Sci & Technol Co LTD, Qingdao, Shandong, Peoples R China
来源
2019 IEEE 28TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) | 2019年
基金
中国国家自然科学基金;
关键词
5G wireless communication technology; Intelligent communication; Cloud platform; Control system;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the continuous development of science technology and economy, the number of automobiles has increased year by year, urban road traffic has become increasingly congested, traffic accidents have occurred frequently, and the emergence of intelligent transportation systems has effectively alleviated traffic pressure and reduced accident rates. This paper designs an intelligent traffic control system based on 5G network. The system includes sensor data acquisition module, 5G communication network transmission module, cloud platform module and lower machine. The test results showed that the system can upload multiple vehicles' driving state parameters to the cloud platform, and the big data is formed. The cloud platform can decide and control the driving state of multiple vehicles through system of real-time display. Meanwhile, the cloud platform can analysis of big data streams. Finally, the purpose of intelligent transportation system control is achieved. The system provide power for the future unmanned vehicle and the implementation of intelligent transportation system.
引用
收藏
页码:1950 / 1955
页数:6
相关论文
共 50 条
[41]   Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm [J].
Changxi Ma ;
Ruichun He .
Neural Computing and Applications, 2019, 31 :2073-2083
[42]   Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm [J].
Ma, Changxi ;
He, Ruichun .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (07) :2073-2083
[43]   Distribution network control system scheduling strategy [J].
Liang, Zhuhong ;
Guo, Yuanling ;
Yang, Yong ;
Chen, Guoyu .
PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019), 2019, :1424-1428
[44]   Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control [J].
Ali, Akbar ;
Qureshi, Muhammad Ahsan ;
Shiraz, Muhammad ;
Shamim, Azra .
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2021, 32
[45]   A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework [J].
Zhu, Feng ;
Aziz, H. M. Abdul ;
Qian, Xinwu ;
Ukkusuri, Satish V. .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2015, 58 :487-501
[46]   Simulation Research on Network Intrusion Prevention and Fluctuation Control System Based on Cloud Computing Architecture [J].
Wei, Ming ;
Yuan, Hui .
2019 11TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2019), 2019, :204-207
[47]   The Economical Electrical Power Control System of Building Illumination based on the RF Wireless Correspondence Network [J].
Shao Lanyun ;
Lv Hongli ;
Zhao Xiuzhen .
2011 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, 2011, :2223-2225
[48]   Neural network based control system architecture proposal for underwater ship hull cleaning robot [J].
Roznowski, G .
EXPERIENCE OF DESIGNING AND APPLICATION OF CAD SYSTEMS IN MICROELECTRONICS, 2003, :368-370
[49]   A holistic approach for analyzing communication utilization in power system control [J].
Ericsson, GN .
IEEE TRANSACTIONS ON POWER DELIVERY, 1998, 13 (04) :979-983
[50]   Design and Implementation of Control System in a Satellite Voice Communication Terminal [J].
Zhang, Lirong ;
Hu, Zhengqun .
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON ELECTRONIC, MECHANICAL, INFORMATION AND MANAGEMENT SOCIETY (EMIM), 2016, 40 :315-319