Deep learning-based short-term traffic flow prediction method

Abstract

The present invention discloses a deep learning method-based short-term traffic flow prediction method. The influence of the traffic flow rate change of the neighbor points of a prediction point, the time characteristic of the prediction point and the influence of the periodic characteristic of the prediction point on the traffic flow rate of the prediction point are considered simultaneously. According to the deep learning method-based short-term traffic flow prediction method of the invention, a convolutional neural network and a long and short-term memory (LSTM) recurrent neural network are combined to construct a Conv-LSTM deep neural network model; a two-way LSTM model is used to analyze the traffic flow historical data of the point and extract the periodic characteristic of the point; and a traffic flow trend and a periodic characteristic which are obtained through analysis are fused, so that the prediction of traffic flow can be realized. With the method of the invention adopted, the defect of the incapability of an existing method to make full use of time and space characteristics can be eliminated, the time and space characteristics of the traffic flow are fully extracted, and the periodic characteristic of the data of the traffic flow is fused with the time and space characteristics, and therefore, the accuracy of short-term traffic flow prediction results can be improved.

Description

technical field [0001] The invention relates to the fields of intelligent transportation and deep learning, in particular to a short-term traffic flow prediction method based on deep learning methods. Background technique [0002] With the continuous development of the economy and the continuous improvement of the level of urbanization, people's demand for transportation is higher, and the frequency of driving is also higher and higher. The problem that follows is that traffic congestion is becoming more and more serious. How to grasp traffic information, Planning travel time and travel routes with reference to traffic information is a problem that needs to be solved at present. Providing users with accurate and real-time traffic flow change forecasts can save travel time for users and reduce unnecessary waste. At the same time, accurate traffic flow information is crucial to It is also very helpful in maintaining traffic support and traffic management, and has great commerc...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a short-term traffic flow prediction method based on deep learning, which uses the temporal and spatial correlation information of urban road traffic flow for prediction, which can overcome the inability of existing methods to make full use of the temporal and spatial characteristics and periodic characteristics of traffic flow data At the same time, different features of traffic flow data are further integrated to improve the accuracy of short-term traffic flow prediction

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