Short-term traffic flow control method based on graph convolution recurrent neural network

Abstract

The invention relates to the field of short-term traffic flow control, in particular to a short-term traffic flow control method based on a graph convolution recurrent neural network. The method comprises the following steps of acquiring a data source; constructing a graph structure traffic flow sequence; constructing a recent component model of a space-time graph convolution recurrent network, adaily cycle component model of the space-time graph convolution recurrent network and a weekly cycle component model of the space-time graph convolution recurrent network according to the multilevel of the graph structure traffic flow sequence in the time dimension; fusing the results of the 3 models to obtain a short-term traffic flow prediction model; obtaining a prediction result according to the model; and counting the predicted data, sending a counted result to a traffic department, and controlling the traffic flow of each check point of a road network. The traffic flow recent, daily cycle and weekly cycle dependencies can be simultaneously modelled by utilizing the space-time graph convolution recurrent neural network, the short-term flow prediction model based on the space-time graph convolution recurrent neural network integrated with multiple component data is established, thereby reaching a precise prediction result.

Description

technical field [0001] The invention relates to the field of short-term traffic flow control, in particular to a short-term traffic flow control method based on a graph convolutional cyclic neural network. Background technique [0002] With the sustained and rapid development of the social economy, the number of cars continues to increase, and the traffic flow on the road also increases, which brings a series of traffic problems. Without changing the current road network, it is one of the effective ways to solve traffic problems by using the intelligent traffic control system to control and induce the short-term traffic flow in the road network. At the same time, accurate traffic flow forecast information can not only improve the travel efficiency of the public, but also provide a reference for the transportation department to formulate management plans and rationally allocate traffic resources. The short-term traffic flow prediction aims to use appropriate methods to predi...

AI Technical Summary

Problems solved by technology

[0004] However, the road network model based on Euclidean distance cannot reflect the spatial connectivity between intersections. Even if convolution is performed on the grid, due to the compromise of data modeling, it can only roughly capture spatial locality. In the process of network modeling, the spatio-temporal characteristics of vehicles passing through the bayonet are not considered, and there is a problem of poor accuracy of the constructed model

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