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

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

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 m...

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Bibliographische Detailangaben
Hauptverfasser: KHOSHMAGHAM, SHAYAN, SYMES, TIFFANY E, HOSSEINI, POUYAN
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
MEASURING
METEOROLOGY
PHYSICS
SIGNALLING
TESTING
TRAFFIC CONTROL SYSTEMS
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Beschreibung
Zusammenfassung: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