Traffic congestion is always a big problem to be solved in the world because of its negative effects. There are many ways to solve traffic congestion based on its causes. And one of the important factors needed to reduce traffic congestion is the control of traffic on the road at a reasonable level. There have been many algorithms proposed to deal with this problem. However, any algorithm has its own limitations such as slow processing time, no timeliness, complicated implementation methods, etc. In recent years, with the explosion of data, many tools and methods are also developed to handle big data. This has opened up many more solutions for traffic-related problems. In this paper, a real-time traffic flow prediction system is proposed with high accuracy, simple method and vivid visualization. The performance of the proposed system is verified through experiments results.


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    Titel :

    Real-Time Traffic Flow Prediction Using Big Data Analytics


    Weitere Titelangaben:

    Lecture Notes on Data Engineering and Communications Technologies


    Beteiligte:
    Nguyen, Ngoc-Thanh (Herausgeber:in) / Dao, Nhu-Ngoc (Herausgeber:in) / Pham, Quang-Dung (Herausgeber:in) / Le, Hong Anh (Herausgeber:in) / Tran, Dang-Khoa (Autor:in) / Hoang, Dinh-Quang (Autor:in) / Le, Viet-Thang (Autor:in) / Thai, Minh-Duc Nguyen (Autor:in) / Do, Trong-Hop (Autor:in)

    Kongress:

    International Conference on Intelligence of Things ; 2022 ; Hanoi, Vietnam August 17, 2022 - August 19, 2022



    Erscheinungsdatum :

    23.08.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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