Traffic congestion prediction has already become a significant aspect of modern transportation systems. By predicting traffic congestion, transportation planners and traffic management agencies can take steps to reduce congestion and improve traffic flow, and also inform the public about expected delays and suggest alternative routes, helping people to make more informed decisions about their travel plans. A growing body of literature has provided different methods in order to improve congestion management abilities to intelligent traffic systems, it is a trending research topic facilitating the development of transportation system prediction. This paper reviews and analyzes broadband of published articles regarding different approaches focusing on short-term real-time traffic predictions; then stresses on five core methodologies for a detailed analysis and provides a discussion on the pros and cons among different approaches with test results. In addition, this paper develops a perspective synthesis of the current status quo that could be the next steps for a more accurate, more efficient prediction. In the end, the paper yields conclusions about possible future research endeavors.


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    Titel :

    Advancing Short-Term Traffic Congestion Prediction: Navigating Challenges in Learning-Based Approaches


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Huang, De-Shuang (Herausgeber:in) / Premaratne, Prashan (Herausgeber:in) / Yuan, Changan (Herausgeber:in) / Wang, Chen (Autor:in) / Atkison, Travis (Autor:in) / Duan, Qiuhua (Autor:in)

    Kongress:

    International Conference on Applied Intelligence ; 2023 ; Nanning, China December 08, 2023 - December 12, 2023



    Erscheinungsdatum :

    01.03.2024


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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