Rail transit passenger flow prediction method based on dynamic hypergraph convolution network

Abstract

The invention provides a rail transit passenger flow prediction method based on a dynamic hypergraph convolutional network, relates to the field of deep learning and the like, and particularly relatesto a hypergraph representation and graph convolutional network-oriented flow prediction task. According to the method, on the basis of carrying out high-order representation on the topological relation of the rail transit network by utilizing a hypergraph, the introduction of a graph convolution network is realized through a hypergraph convolution module, and a dynamic hypergraph convolution network mechanism is realized by excavating the internal space-time characteristics of passenger flow OD to construct a dynamic hyperedge. Compared with a traditional mathematical model and a traditionalmachine learning method, the method is deeper and more accurate in rail transit feature extraction. The task of rail transit passenger flow prediction is completed, and the prediction accuracy is improved.

Description

technical field [0001] The invention relates to the fields of graph theory, deep learning and the like, especially the task of forecasting passenger flow in and out of stations for rail transit. Background technique [0002] Prediction of passenger flow in and out of rail transit stations is one of the research hotspots in the field of smart transportation. Accurate passenger flow prediction methods will help the transportation system to carry out reasonable route scheduling, road network design and crowd evacuation regulation and other specific applications. Previous related technologies mostly focused on methods based on mathematical modeling and machine learning. However, in terms of rail transit, due to the unique topological structure of underground rail transit and the travel patterns of passengers, it is difficult to obtain efficient and accurate prediction results by simple application of traditional methods, and related research is relatively limited. [0003] The ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of spatial topology representation and dynamic temporal feature extraction, we use hypergraphs to model and represent rail transit data to replace traditional non-graph and simple graph methods

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