A framework for modeling traffic speed in a transportation network analyzes both the spatial and temporal dependencies in probe-based traffic speeds, historical weather data, and forecasted weather data, using multiple machine learning models. A decentralized partial least squares (PLS) regression model predicts short-term speed using localized, historical probe-based traffic data, and a deep learning model applies the predicted short-term speed to further estimate traffic speed at specified times and at specific locations in the transportation network for predicting traffic bottlenecks and other future traffic states


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    Titel :

    SHORT-TERM TRAFFIC SPEED PREDICTION AND FORECASTING USING MACHINE LEARNING ANALYSIS OF SPATIOTEMPORAL TRAFFIC SPEED DEPENDENCIES IN PROBE AND WEATHER DATA


    Beteiligte:

    Erscheinungsdatum :

    06.06.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G01W METEOROLOGY , Meteorologie / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen







    Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms

    Rivindu Weerasekera / Mohan Sridharan / Prakash Ranjitkar | DOAJ | 2020

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