Graph learning-based graph convolutional neural network traffic prediction method and system

Abstract

The invention discloses a graph learning-based graph convolutional neural network traffic prediction method and system, and the method comprises the steps: obtaining a more accurate new adjacent matrix through the learning of a graph learning module, capturing a plurality of spatial relationships through the graph learning module, and improving the traffic prediction precision; meanwhile, the space-time convolution block comprises two space-time convolution layers and a space graph convolution layer, and the space-time convolution layers are obtained by combining expansion convolution and a gating mechanism, so that long-time dependence can be effectively captured, and training time and space resources are saved.

Description

technical field [0001] The invention belongs to the technical field of traffic forecasting, and in particular relates to a graph learning-based graph convolutional neural network traffic forecasting method and system. Background technique [0002] Traffic prediction is an important part of intelligent transportation system, and accurate traffic speed prediction can provide meaningful reference information for traffic management, traffic control, and traffic planning. Traffic forecasting is a challenging problem due to complex temporal and spatial dependencies. [0003] Existing traffic forecasting can be divided into traditional machine learning methods and deep learning methods. Deep learning methods include recurrent network (RNN) and its variants based on long short-term memory network (LSTM) and gated recurrent unit (GRU), convolutional neural network (CNN), graph convolutional neural network (GCN) and their combination . [0004] RNN-based methods suffer from time-co...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a graph convolution neural network traffic prediction method and system based on graph learning to solve the problem that the existing method only considers a single spatial relationship and cannot capture multiple spatial relationships existing in the traffic network. There are problems with methods that are not effective enough in capturing long-term time dependencies

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