Short-time traffic flow prediction method based on space-time correlation and convolutional neural network

Abstract

The invention discloses a short-time traffic flow prediction method based on space-time correlation and a convolutional neural network. The method comprises: mining space-time correlation in the traffic flow data; traffic flow data is converted into a two-dimensional matrix with space-time traffic flow information, the space-time characteristic information of traffic flow in the matrix is extracted, processed and learned by utilizing the convolutional neural network, a prediction result is finally obtained, and in addition, optimal input data is screened out by adopting a space-time characteristic selection algorithm. The randomness and uncertainty of the traffic flow data are deeply mined, and the space-time correlation in the traffic flow data is fully considered, so that the predictionprecision of the traffic flow data is effectively improved.

Description

technical field [0001] The invention belongs to the field of traffic control and relates to a short-term traffic flow prediction method based on spatio-temporal correlation and a convolutional neural network. Background technique [0002] The changing trend of future traffic has always been a concern of traffic management departments and travelers. Reliable traffic forecast information can provide a reference for traffic management departments to formulate management plans and rationally allocate traffic resources; at the same time, travelers can plan their trips reasonably based on traffic forecast information. Accurate and timely traffic flow data can be used to help alleviate road congestion, reduce vehicle emissions, and improve road efficiency. With the deployment of intelligent transportation systems, the research on short-term traffic flow prediction models is of great importance. The acquisition of real-time and accurate short-term traffic flow prediction data is ch...

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Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, the present invention provides a short-term traffic flow prediction method based on spatio-temporal correlation and convolutional neural network, which solves the problem that traditional models are difficult to make full use of the inherent time and space in traffic flow data The question of relevance

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