Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.


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    Title :

    Traffic prediction using real-world transportation data


    Contributors:
    PAN BEI (author) / DEMIRYUREK UGUR (author) / SHAHABI CYRUS (author)

    Publication date :

    2016-03-15


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    Traffic prediction using real-world transportation data

    PAN BEI / DEMIRYUREK UGUR / SHAHABI CYRUS | European Patent Office | 2018

    Free access


    Real-Time Traffic Data Collection for Transportation Telematics

    Westerman, M. | British Library Conference Proceedings | 1996