Deep learning method for predicting urban traffic jam based on GPS

Abstract

The invention discloses a deep learning method for predicting urban traffic jam based on GPS, and relates to the field of intelligent traffic management. Through the deep learning method, a unsupervised large-scale urban network traffic jam prediction method (CRC3D) is innovatively constructed based on GPS real-time data on urban taxies. The method is characterized in that a recursion neural network (RNN), a convolutional neural network (CNN) and a three-dimensional convolutional neural network (C3D) are combined. The recursion neural network and the three-dimensional convolutional neural network can efficiently capture time-varying characteristics of continuous traffic states from different angles. The method is good in effect and high in practicability.

Description

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AI Technical Summary

Benefits of technology

This invention relates to improving road networks by utilizing advanced technology such as location sensors or cameras that track vehicles over their route. By combining these technologies into an algorithm called Deep Learning Network (DLN), it becomes possible to predict how well roads will be used without being limited only at certain points along its way. Overall, DLNA helps improve transportation efficiency while reducing delays caused by heavy usage patterns like car accidents.

Problems solved by technology

The technical problem addressed by this patented method relates to predicting how well different parts or areas are being used for better road use efficiency during heavy usage times such as commuting between cities without causing delays due to overcrowding at intersections where there may be multiple lanes intersecting with each other. Traditional approaches involve simplifying certain aspects like lane assignment based upon geometry but also neglects important factors that affect driving behavior (such as speed).

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