Intelligent traffic system flow short-term prediction method and system based on divergence convolution and GAT

Abstract

The invention discloses an intelligent traffic system flow short-term prediction method and system based on divergence convolution and GAT. Missing values in the traffic flow data set are complementedthrough a statistical correlation method such as a historical average value method, the distance between every two nodes is calculated, a Gaussian kernel with a threshold value is used for establishing an adjacency matrix representing the node adjacency degree, a divergence convolution layer is used for fully extracting spatial and temporal features of traffic flow data, and then, the extracted features are predicted through a coder-decoder program with planned sampling based on a graph attention mechanism and a divergence convolution gating cycle unit network. According to the method, the problem of data missing can be effectively improved, the prediction precision and the operation efficiency are remarkably improved, and particularly, the short-time prediction effect is better.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence forecasting algorithms, in particular to a short-term traffic forecasting method and system for an intelligent transportation system based on divergent convolution and a Graph Attention Network (GAT) algorithm. Background technique [0002] In recent years, with the continuous improvement of science and technology and economic level, people's transportation methods have been continuously enriched, and the number of motor vehicles owned by urban residents has also undergone rapid changes. This makes people's life more convenient, but at the same time creates many social problems, such as traffic congestion, traffic accidents, excessive energy consumption and excessive carbon emissions. Among them, the phenomenon of urban traffic congestion is becoming more and more serious, ranging from increasing people's travel time and affecting schedules, to causing traffic accidents, affecting ...

AI Technical Summary

Problems solved by technology

[0006] However, the current mainstream traffic flow prediction methods are not particularly effective, because the spatial correlation of road network segments is difficult to capture, the nonlinear temporal dynamics that change with road conditions are difficult to simulate, and the inherent difficulties of multi-step prediction

More importantly, due to various reasons, some data sets have data missing, which increases the difficulty of model training and adversely affects the analysis and data mining of traffic flow data. Technical problems such as the accuracy and computational efficiency of the flow forecasting model are not high enough

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