Traffic prediction method of spatio-temporal diagram convolution model based on attribute enhancement

Abstract

The invention discloses a traffic prediction method of a spatio-temporal graph convolution model based on attribute enhancement. The method comprises the following steps: constructing an adjacent matrix A based on road network data; constructing an attribute enhancement matrix Kt = [Xt, p, Bt] at the moment t based on the feature matrix X, the interest point information vector p and the weather information matrix B; inputting the attribute enhancement matrixes of the n historical moments and the adjacent matrix of the road network into a spatio-temporal diagram convolution model for learning and training, calculating a traffic flow hidden state, and obtaining a traffic prediction value. According to the method of the invention, on the basis of spatial features when a spatio-temporal diagram convolution model is used for modeling, multi-source fragmented city data are fused to capture the relationship between external factors influencing traffic and traffic flow, the perception of the spatio-temporal diagram convolution model on the external influence factors is enhanced, and therefore, more efficient and accurate traffic prediction can be realized.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a traffic prediction method based on an attribute-enhanced spatio-temporal graph convolution model. Background technique [0002] In recent years, with the rapid development of urban traffic roads, the imbalance between people, vehicles and roads has become increasingly prominent. The resulting traffic problems have brought great inconvenience to people's life and work, and even seriously affected the lives of urban residents. quality. Intelligent transportation system can be regarded as an important way to solve a series of urban traffic problems. As one of the important components of intelligent transportation system, traffic flow prediction can provide scientific basis for the management, control and planning of urban transportation system, and is one of the key technologies for building traffic command information platform and traffic guidance service plat...

AI Technical Summary

Problems solved by technology

Although the traffic flow prediction method based on deep neural network can solve the limitations of traditional methods in traffic flow prediction and enhance the ability to understand the spatio-temporal characteristics of traffic flow data, it still has limitations in comprehensively considering multiple influencing factors.

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