Bidirectional traffic flow dynamic modeling method based on deep space-time diagram neural network

Through the two-way traffic flow dynamic modeling method based on deep spatio-temporal graph neural network, the problem of insufficient modeling of two-way traffic flow in the existing technology is solved, and more efficient and accurate traffic flow prediction is achieved, improving the applicability of the model and adaptability to complex traffic scenarios.

CN119694117AActive Publication Date: 2025-03-25HANGZHOU DIANZI UNIV
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Patent Information

Application Number
CN202411813552.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-03-25
Estimated Expiration
2044-12-10

AI Technical Summary

Technical Problem

The prior art ignores the modeling of two-way traffic flows in traffic flow prediction, and lacks the capture of the correlation between dual-flow data, resulting in poor adaptability of the model in complex traffic scenarios.

Method used

The two-way traffic flow dynamic modeling method based on the deep spatiotemporal graph neural network is adopted. By constructing the traffic flow dynamic graph, local spatiotemporal dependence is captured, and time and spatial characteristics are extracted using the time convolution layer and composite graph convolution module, the two-way flow is fused, and the traffic prediction results are finally obtained through the prediction layer.

Benefits of technology

It improves the accuracy of traffic flow prediction and the applicability of the model, can more effectively capture the dependence between two-way traffic data, and improves the ability to predict multi-directional flow and the dynamic relationship of traffic flow patterns.

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Abstract

The invention discloses a two-way traffic flow dynamic modeling method based on a deep space-time diagram neural network, and belongs to the field of intelligent traffic and data mining, and the method comprises the following steps: S1, constructing a traffic flow dynamic diagram, and capturing the local space-time dependence of a traffic flow; s2, after the local space-time dependency is obtained, extracting time features by using a time convolutional layer; s3, after the time features are obtained, a selection gate is used for screening the effective information obtained in the step S2, and final output is obtained; s4, after a result in S3 is obtained, using a composite graph convolution module to extract spatial features; s5, fusing the two-way traffic, and obtaining a final prediction result through a prediction layer; s6, the loss of the model is calculated, and whether the model meets the requirement is determined, the problems that in the prior art, in traffic flow prediction, bidirectional flow modeling is insufficient, dynamic dependency capture is insufficient, and adaptability to complex traffic scenes is poor are solved, and therefore the traffic flow prediction precision and the model applicability are improved.
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