Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (12): 3287-3294.doi: 10.23940/ijpe.19.12.p21.32873294

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Multi-Dimensional and Multi-Scale Modeling of Traffic State in Jiangxi Expressway based on Vehicle Network

Zhaozheng Chena,*, Yuanyuan Wangb, Zhengyu Tana, and Yuejin Zhangc   

  1. aJiangxi Expressway Networking Management Center, Nanchang, 330000, China;
    bJiangxi Provincial Transportation Department Emergency Command Center, Nanchang, 330000, China;
    cSchool of Information Engineering, East China Jiaotong University, Nanchang, 330000, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address: chenzhaozheng545@163.com

Abstract: In order to improve the big data analysis and traffic data management ability of the Jiangxi high-speed traffic condition monitoring platform, a multi-dimensional multi-scale analysis model of Jiangxi high-speed traffic condition characteristics based on vehicle networking and traffic big data information fusion is proposed. On the basis of building a good knowledge base, model base, and method base, the model base feature analysis and dynamic detection method are used to effectively mine big data of the Jiangxi high-speed traffic status monitoring platform. Combined with information fusion theory, the multi-dimensional model of the Jiangxi high-speed traffic status monitoring platform based on text information, location information, picture, audio, video, and other Jiangxi high-speed traffic status data can be realized. The test results show that big data mining in the platform with this method has better clustering and achieves high multi-dimensional multi-scale fusion of high-speed traffic condition characteristics in Jiangxi. The efficiency of data scheduling and access are improved effectively, enhancing the multi-dimensional multi-scale analysis and resource scheduling ability of Jiangxi high-speed traffic condition characteristics.

Key words: car networking, Jiangxi high-speed traffic, state characteristics, multi-dimensional multi-scale, big data