Non-stationary traffic prediction method based on wave flow decomposition and time delay perception

Through the non-stationary traffic prediction method based on wave current decomposition and delay perception, the decoupled flow layer and adaptive graph convolution network are used to solve the shortcomings of traditional traffic prediction methods in non-stationary and space-time delay effects, more accurate traffic flow prediction is achieved, and the management capabilities of intelligent traffic systems are improved.

CN120409841AActive Publication Date: 2025-08-01WUXI UNIV
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Patent Information

Application Number
CN202510899425.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-01
Publication Date
2025-08-01
Estimated Expiration
2045-07-01

AI Technical Summary

Technical Problem

Traditional traffic flow prediction methods have problems such as insufficient prediction accuracy and poor adaptability to emergencies when dealing with non-stationarity and space-time lag effects, which is difficult to meet the refined and intelligent needs of modern traffic management.

Method used

Using a non-stationary traffic prediction method based on wave current decomposition and delay perception, the spatial and temporal correlation and dynamic change characteristics of traffic flow data are captured by decoupling the flow layer, delay perception directed graph attention and adaptive graph convolution network, and the adaptive graph convolution network and time-gated convolution module are built, which integrates short-term fluctuations and long-term trend characteristics to conduct traffic flow prediction.

Benefits of technology

It improves the accuracy and robustness of traffic flow prediction, can better cope with complex traffic networks, provide more reliable short- and long-term traffic flow prediction, and improves the scientific nature of traffic management decisions and the operation efficiency of intelligent traffic systems.

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Abstract

The invention provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.
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