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

A convolutional network and rail transit technology, applied in the field of graph theory and deep learning, to achieve the effect of improving prediction accuracy and improving accuracy

Pending Publication Date: 2020-10-30
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] In order to solve the problems of spatial topology representation and dynamic temporal feature extraction...

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

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[0035] Rail traffic flow prediction is a fundamental problem in the field of smart transportation. The dynamic hypergraph convolution network proposed by the method of the present invention can model the topological structure and passenger flow characteristics of rail transit, and realize the improvement of prediction accuracy. We actually used the historical data of rail transit passenger flow in Beijing in July, September, and December of 2015 for a total of three months to train the dynamic hypergraph convolutional network and test the accuracy of the prediction results.

[0036]The input of the dynamic hypergraph convolutional network is the traffic flow data represented by the subway passenger flow and the traffic network structure represented by the subway line structure. The traffic flow data is analyzed by OD, and the travel rules of large passenger flow are obtained through clustering algorithm, and the hyperedge is implied by this. Taking the morning peak period in ...

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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 ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G06N3/08
CPCG06Q10/04G06Q50/30G06N3/049G06N3/08G06N3/045
Inventor 张勇王竟成魏运胡永利尹宝才
Owner BEIJING UNIV OF TECH
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