Traffic prediction method based on multi-scale graph convolutional network model

Abstract

The invention discloses a traffic prediction method based on a multi-scale graph convolutional network model, and the method comprises the steps: employing a multi-scale graph convolutional network and a GRU, and capturing the time dependence of a traffic network, i.e., the local time change trend of a traffic flow, and the spatial dependence, i.e., a topological space structure at the same time, and predicting the traffic flow of each road section in the future time step through the traffic flow of the historical time step, thereby accurately predicting the traffic flow of the road network. The method can effectively predict the temporal and spatial change characteristics and rules of the traffic flow, is high in prediction precision, and improves the traffic flow prediction effect.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation and relates to traffic prediction technology, in particular to a traffic prediction method based on a multi-scale graph convolutional network model. Background technique [0002] With the rapid development of intelligent transportation systems, traffic forecasting has attracted more and more attention. It is an important part of traffic management system and an important part of traffic planning, traffic management and traffic control. Traffic forecasting can not only provide a scientific basis for traffic managers to perceive traffic congestion in advance and restrict vehicles, but also help travelers choose appropriate travel routes, thereby improving travel efficiency. However, complex spatiotemporal dependencies in road networks complicate traffic prediction. Spatial dependence means that the change of traffic flow is limited by the topological structure of the urban road n...

AI Technical Summary

Problems solved by technology

However, the existing traffic prediction methods using the above models only consider the time dependence of traffic flow, but ignore its spatial dependence, resulting in the prediction results unable to reflect the actual constraints of the urban road network on traffic data changes. Therefore, the present It is difficult to accurately predict the traffic status of the road network with model methods, and the prediction effect is not good

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