Having the ability to accurately predict the traffic situation on urban roads can be useful for creating administrative traffic control systems, navigation software solutions or for general public awareness. In this paper we are describing our methodology for creating a new experimental setup and then analyzing the performance results of different neural network models for predicting Traffic Indexes calculated from public transportation positioning data as time series.


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    Titel :

    Predicting Traffic Indexes on Urban Roads Based on Public Transportation Vehicle Data in Experimental Environment


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arai, Kohei (Herausgeber:in) / Yosifov, Georgi (Autor:in) / Petrov, Milen (Autor:in)

    Kongress:

    Science and Information Conference ; 2022 July 14, 2022 - July 15, 2022


    Erschienen in:

    Intelligent Computing ; Kapitel : 8 ; 159-168


    Erscheinungsdatum :

    2022-07-07


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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