Abstract: Provided is a system for warning about intersection danger on the basis of behavior prediction. The system includes a sensor for detecting and predicting actions of surrounding objects, such as vehicles and pedestrians, and a function of providing an alarm feedback and can perform independent computation due to a separate processor installed therein for edge computing.

Abstract: 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.

Abstract: The present invention relates to a system for forecasting the traffic condition pattern by an analysis of traffic data.

Abstract: The present invention relates to a system for forecasting the traffic condition pattern by an analysis of traffic data.