A method of predicting traffic congestion and controlling traffic signals based on deep learning according to an embodiment of the present invention includes: analyzing regional network outflow behavior based on a per-intersection traffic demand pattern or traffic signal control, and determining a specific intersection to be a control target intersection based on the results of the analysis; generating 2D space-time images by analyzing the data of predetermined customized composite data corresponding to the control target intersection and a plurality of pieces of image data for the control target intersection in terms of time and space; generating a real-time traffic congestion index by using the 2D space-time image of the control target intersection and the 2D space-time image of the data of the customized composite data corresponding to the control target intersection; and controlling the traffic signals of the control target intersection based on the real-time traffic congestion index.


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    Title :

    Method of predicting traffic congestion and controlling traffic signals based on deep learning and server for performing the same


    Contributors:

    Publication date :

    2021-01-26


    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 / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V