Traffic flow prediction model for multilayer space-time structure correlation perception

Through multi-layer spatiotemporal structure decomposition and bidirectional feature modulation traffic flow prediction model, the prediction accuracy problem of existing models in complex traffic scenarios is solved, and efficient prediction of traffic flow is achieved.

CN120449124APending Publication Date: 2025-08-08ZHAOQING UNIV
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Patent Information

Application Number
CN202510586520.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-08
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The existing traffic flow prediction model fails to effectively capture multi-level spatial dependencies and time-period characteristics, resulting in limited prediction accuracy in complex topological changes or periodic perturbations scenarios.

Method used

Multi-layer spatiotemporal structure decomposition layer is adopted to extract road hierarchical perception convolution network and variational modal decomposition algorithm, and the hidden spatial topological relationship is mined in combination with the graph attention network, and spatiotemporal coupled feature tensors are generated through adaptive adjacency matrix generation and bidirectional feature modulation, and input the spatiotemporal graph convolution prediction network for prediction.

Benefits of technology

Synchronous modeling of spatial structure levels and non-stationary time periods in traffic flow is realized, the model's adaptability and prediction accuracy under the changes in complex traffic patterns is improved, and the generalization ability across regions and time periods is improved.

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Abstract

The invention relates to the technical field of traffic prediction, in particular to a multi-layer space-time structure correlation perception traffic flow prediction model, which comprises a space-time structure decomposition layer for decomposing original traffic flow data into a road hierarchical structure feature matrix, a dynamic time period feature matrix and a hidden space topology feature matrix; the dynamic adjacency matrix generation layer is used for constructing a self-adaptive adjacency weight matrix based on the hidden space topological feature matrix; the cross-level correlation perception layer performs bidirectional feature modulation on the road hierarchical structure feature matrix and the dynamic time period feature matrix to generate a space-time coupling feature tensor; and the prediction layer inputs the space-time coupling feature tensor into a space-time diagram convolution prediction network and outputs a traffic flow prediction value in a future time period. According to the method, the expression ability of the model on potential heterogeneous association between the nodes is improved, and the generalization ability across regions and time periods is effectively improved.
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