Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction


    Beteiligte:


    Erscheinungsdatum :

    2014




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    TRAFFIC FLOW PREDICTION DEVICE AND TRAFFIC FLOW PREDICTION SYSTEM

    OKUDE MARIKO / NAGAI TORU / SAKAMOTO TOSHIYUKI et al. | Europäisches Patentamt | 2019

    Freier Zugriff


    TRAFFIC FLOW PREDICTION DEVICE, TRAFFIC FLOW PREDICTION METHOD, AND PROGRAM

    MATSUDAIRA MASAKI / HAYASHI MASAHIRO / MASUDA JUNKI | Europäisches Patentamt | 2020

    Freier Zugriff

    TRAFFIC FLOW PREDICTION SYSTEM, TRAFFIC FLOW PREDICTION METHOD, AND PROGRAM

    MATSUDAIRA MASAKI / OKANO KENGO | Europäisches Patentamt | 2023

    Freier Zugriff

    Urban traffic road network traffic flow prediction method considering carbon emission model

    LI RUI / HU YUEGUI / YANG ZHIJIA et al. | Europäisches Patentamt | 2024

    Freier Zugriff