Traffic flow prediction method of divergence convolution recurrent neural network based on space-time diagram

Abstract

The invention discloses a traffic flow prediction method of a divergence convolution recurrent neural network based on a space-time diagram. The traffic flow prediction method is characterized in thata directed weighted graph of a road network is constructed based on spatial features of a traffic network, then a traffic flow prediction model of a graphic divergence convolution recurrent neural network is constructed by taking the directed weighted graph as a basic unit of prediction, deep learning is carried out by means of time-space characteristics of the traffic network, time-space prediction is carried out on the traffic flow of the traffic road network, a final traffic flow prediction model is constructed, and real-time prediction of the traffic flow is realized. The traffic flow prediction method has the advantages of precise prediction and high fitting degree.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a traffic flow prediction method based on a spatio-temporal graph-based divergent convolutional cyclic neural network. Background technique [0002] Traffic flow prediction has important practical significance in the field of sustainable intelligent transportation. Traffic flow forecasting is one of the typical examples of this kind of continuous learning and improvement. Due to (1) the nonlinear time dynamics caused by the constantly changing road conditions; (2) the complex spatial dependence of the road network topology; With the rapid development of management technology, it is possible to process and apply massive traffic data in real time. Therefore, this chapter will combine one of the applications of big data—deep learning method, and propose a traffic flow prediction method based on graph divergent convolutional cyclic neural network , to predict the traffic f...

AI Technical Summary

Problems solved by technology

[0004] ① The spatio-temporal characteristics of traffic flow (time dependence and space dependence) are not considered at the same time, and only the traffic flow data of a certain section are considered microscopically for prediction;

[0005] ② It does not take into account that the traffic flow is a gradually divergent flow process, that is, the traffic flow at the current location will gradually diverge from near to far as the time changes

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