Urban traffic flow prediction method based on Markov clustering graph attention network

Abstract

The invention relates to an urban traffic flow prediction method based on a Markov clustering graph attention network, and the method comprises the following steps: 1, obtaining a time sequence flow matrix according to historical transportation data; 2, natural structure information existing in the graph is extracted based on a Markov clustering algorithm idea, and a global correlation node matrix is obtained; 3, establishing a generative adversarial neural network model, and expanding an improved graph attention module in a model generator to global correlation node information obtained based on a Markov clustering algorithm instead of limiting neighbor nodes in first-order neighbor nodes in a graph attention network when acquiring spatial hidden features; learning the training model, and taking the learned model as a regional traffic flow prediction model; the improved graph attention module not only pays attention to local neighbor nodes, but also dynamically considers neighbor node information in an overall graph structure, and gives different weights to the neighbor nodes, so that the acquisition capability of spatial features is improved.

Description

technical field [0001] The invention relates to a method for predicting urban traffic flow based on a Markov cluster graph attention network, which belongs to the technical field of traffic. Background technique [0002] With the development of the economy and the improvement of people's living standards, the traffic problems caused by travel are becoming more and more serious. The mining and analysis of traffic data has become a hot topic for researchers, and the prediction of traffic data has also become popular. [0003] Traditional statistics-based methods use historical data to predict future trends, such as: Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter (KF), but they cannot handle non-linear traffic data because it is assumed that future predictions are related to The past data have the same characteristics; in recent years, neural network methods based on deep learning, such as graph convolutional neural network (GCN) to extract spatial features ...

AI Technical Summary

Problems solved by technology

However, there are still deficiencies in these methods. GCN only considers the global structure of the graph, and cannot assign different importance to adjacent nodes. GAT can assign different weight values ​​to adjacent nodes, but to a certain extent, it abandons the overall structure of the graph.

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