Urban traffic flow prediction method and medium based on graph convolutional neural network

Abstract

The invention discloses a method and medium for predicting urban traffic flow based on a graph convolutional neural network, wherein the method includes: obtaining original data; generating a distance matrix according to the latitude and longitude information corresponding to each node; calculating according to the average speed limit and the distance matrix Accessibility matrix; construct an initial traffic flow prediction model for predicting traffic flow speed, and input traffic flow speed information and reachability matrix into the initial traffic flow prediction model, so that the initial traffic flow prediction model can output traffic according to the traffic flow speed information and reachability matrix flow velocity prediction value; train the initial traffic flow prediction model to determine the final traffic flow prediction model; input the traffic flow velocity information to be predicted and the reachability matrix to be predicted into the traffic flow prediction model, so that the traffic flow prediction model can predict the future traffic flow Forecasting; realize the effective extraction of the spatial characteristics of the urban traffic road network, improve the accuracy of traffic flow prediction, and improve the universality of the prediction method to make it easy to promote.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a graph convolutional neural network-based urban traffic flow prediction method and a computer-readable storage medium. Background technique [0002] Traffic flow refers to the traffic flow formed by continuous driving of cars on the road. With the increasing number of cars, urban traffic is becoming more and more congested. Therefore, it is particularly important to predict urban traffic flow. For example, through traffic flow prediction, it is convenient for relevant departments to control urban traffic in a directional way; Travel time of motorists, etc. [0003] However, in related technologies, it is difficult to effectively obtain the unique physical characteristics of the traffic road network, resulting in low accuracy of the final predicted traffic flow data; moreover, the requirement for the amount of information of the original data is too high (...

AI Technical Summary

Problems solved by technology

[0003] However, in related technologies, it is difficult to effectively obtain the unique physical characteristics of the traffic road network, resulting in low accuracy of the final predicted traffic flow data; moreover, the requirement for the amount of information of the original data is too high (for example, the adjacency information of sensor nodes etc.), lack of universality

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