As urban landscapes and transportation infrastructures evolve, the precision of traffic flow prediction within Intelligent Transportation Systems (ITS) becomes paramount. It is vital for mitigating traffic congestion and for refining autonomous driving and route planning. Yet, traditional methods struggle with the intricacies and vastness of modern data. This paper surveys traditional approaches before exploring deep learning techniques that adeptly model the nonlinear, spatio-temporal complexities of traffic flows. It examines how deep learning harnesses spatial and temporal data correlations, offering a robust framework for ITS. The discussion concludes with the challenges in traffic prediction and opportunities for future research, charting a course for innovative solutions.


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    Titel :

    A review of deep learning for traffic flow prediction


    Beteiligte:
    Chen, Hao (Herausgeber:in) / Shangguan, Wei (Herausgeber:in) / Zhang, Hao (Autor:in) / Ren, Bin (Autor:in) / He, Chunhong (Autor:in)

    Kongress:

    Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024) ; 2024 ; Xi'an, China


    Erschienen in:

    Proc. SPIE ; 13422


    Erscheinungsdatum :

    20.01.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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