Traffic prediction method based on attention temporal graph convolutional network

Abstract

The invention belongs to the field of intelligent transportation, and discloses a traffic prediction method based on an attention temporal graph convolutional network. The method includes the following steps that: firstly, an urban road network is modeled as a graph structure, nodes of the graph represent road sections, edges are connection relationships between the road sections, and the time series of each road section is described as attribute characteristics of the nodes; secondly, the temporal and spatial characteristics of the traffic flow are captured by using an attention temporal graph convolutional network model, the temporal variation trend of the traffic flow on urban roads is learned by using gated cycle units to capture the time dependence, and the global temporal variation trend of the traffic flow is learned by using an attention mechanism; and then, the traffic flow state at different times on each road section is obtained by using a fully connected layer; and finally,different evaluation indexes are used to estimate the difference between the real value and the predicted value of the traffic flow on the urban roads and further estimate the prediction ability of the model. Experiments prove that the method provided by the invention can effectively realize tasks of predicting the traffic flow on the urban roads.

Description

technical field [0001] The invention relates to a traffic prediction method based on attention temporal graph convolution network, belonging to the technical field of intelligent traffic. Background technique [0002] With the deployment and development of intelligent transportation systems, the task of traffic flow prediction has been paid more and more attention. It is a key part of advanced traffic management systems and an important part of traffic planning, traffic management and traffic control. Traffic flow forecasting is the process of analyzing the traffic status on urban roads, including flow, speed and density, etc., mining traffic operation rules and predicting the trend of traffic status changes on the road. It can not only perceive traffic congestion in advance for traffic managers, limit vehicle It can also provide a scientific basis for urban travelers to choose appropriate travel routes and improve travel efficiency. But accurate real-time traffic flow pred...

AI Technical Summary

Problems solved by technology

However, since CNN is essentially applicable to Euclidean space, such as images, grids, etc., it has limitations on traffic networks with complex topological structures, so it cannot essentially describe the spatial dependence of traffic flow. Therefore, this type of method also exists certain defects

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