A Traffic Prediction Method Based on Attribute Augmented Spatial-Temporal Graph Convolutional Model

Abstract

The invention discloses a traffic prediction method based on an attribute-enhanced spatio-temporal graph convolution model, comprising the following steps: constructing an adjacency matrix A based on road network data; Attribute enhancement matrix K at each moment t =[X t ,p,B t ]; Input the attribute enhancement matrix of n historical moments and the adjacency matrix of the road network into the spatio-temporal graph convolution model for learning and training, calculate the hidden state of traffic flow, and obtain the traffic prediction value. On the basis of using the space-time graph convolution model to model the time-space characteristics, the method of the present invention combines multi-source fragmented urban data to capture the relationship between external factors that affect traffic and traffic flow, and enhance the impact of the spatio-temporal graph convolution model on external factors perception, and thus achieve more efficient and accurate traffic prediction.

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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