Reversible lane traffic flow prediction method based on edge calculation

Through edge computing and generation of adversarial network models, a dynamic road network map is constructed, which solves the problem of insufficient prediction accuracy of traffic flow in tidal lanes, achieves high-precision and real-time traffic flow prediction, and improves traffic management efficiency.

CN120375606AActive Publication Date: 2025-07-25JIANGXI NORMAL UNIV
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Patent Information

Application Number
CN202510865048.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-26
Publication Date
2025-07-25
Estimated Expiration
2045-06-26

AI Technical Summary

Technical Problem

The existing traffic flow prediction methods are mostly based on static road networks, which cannot effectively reflect the changes in space-time flow caused by dynamic adjustment of tidal lanes, resulting in insufficient prediction accuracy and difficult to meet the needs of intelligent transportation systems for real-time scheduling and optimization.

Method used

Multi-source data is obtained through edge computing, a dynamic road network map is built, and a dynamic road network map is combined with the generation of adversarial network models, multi-head attention mechanisms and graph neural networks, and adversarial training is carried out, the model is optimized and deployed to an edge server for prediction.

Benefits of technology

It improves the accuracy and timeliness of traffic flow prediction, enhances the model's perception of complex traffic states, and improves the real-time and response efficiency of traffic management.

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Abstract

The invention discloses a reversible lane traffic flow prediction method based on edge calculation, and relates to the technical field of intelligent traffic. The method comprises the following steps: acquiring reversible lane configuration data, service data, traffic flow data and road network data through an edge server and a cloud server; preprocessing the data; constructing a static road network diagram based on the reversible lane configuration data and the road network data; generating a dynamic road network diagram in combination with time granularity; constructing a generative adversarial network model fusing a multi-head attention mechanism and a graph neural network, and performing adversarial training; and deploying the optimized model to an edge server to realize prediction of the reversible lane traffic flow. According to the method, the precision and timeliness of traffic flow prediction are improved, the perception ability of the model to spatial-temporal characteristics is enhanced, and the improvement of traffic efficiency and the intelligent application of edge resources are promoted.
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