Dynamic traffic dispersing control method and system based on traffic flow situation prediction

By constructing a dynamic effective capacity and multi-layer spatiotemporal graph convolutional neural network, combined with real-time traffic data and physical parameters, the problem of road carrying capacity prediction distortion under severe weather conditions was solved, realizing the pre-control of traffic management and ensuring the stability of urban traffic operation.

CN122347873APending Publication Date: 2026-07-07CHINA NAT CHEM COMM CONSTR GRP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA NAT CHEM COMM CONSTR GRP CO LTD
Filing Date
2026-06-09
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Existing traffic flow prediction and diversion technologies fail to effectively integrate the three-dimensional road geometry of cities with the physical attenuation law of underlying forces under extreme weather conditions. This results in distorted predictions of the effective dynamic traffic capacity of roads under adverse terrain and weather conditions, causing serious delays in traffic diversion and diversion instructions. This can easily lead to overflow deadlocks at urban underpasses and ground intersections.

Method used

By acquiring real-time traffic flow velocity, road surface friction coefficient, average vehicle wheelbase, and three-dimensional road longitudinal slope angle, a dynamic effective capacity is constructed. Traffic flow is predicted using a multi-layer overlay spatiotemporal graph convolutional neural network. Overflow prevention and diversion control is performed based on corrected weights and physical saturation parameters. Phase duration compensation parameters are generated to execute overflow prevention, interception, and diversion control.

Benefits of technology

It enables accurate prediction of changes in road carrying capacity under severe weather conditions, early prevention of congestion, reduction of traffic management delays, ensuring the orderly operation of core urban traffic, and avoiding traffic overflow and deadlock at traffic nodes.

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Abstract

The application belongs to the technical field of traffic management, and particularly relates to a dynamic dredging control method and system based on traffic flow situation prediction, which comprises the following steps: collecting multiple source physical parameters, reconstructing road node dynamic effective capacity based on the multiple source physical parameters, obtaining a dynamic physical resistance difference value according to the difference between the corresponding physical saturation parameters of adjacent nodes, replacing the physical distance parameters with the dynamic physical resistance difference value to generate a correction weight, inputting a graph adjacency matrix containing the correction weight characteristics and historical traffic flow time series data matrix into a graph convolution calculation layer to output predicted traffic flow, comparing the predicted traffic flow with the dynamic effective capacity to generate a capacity overflow difference value, and issuing a control instruction to execute a flood prevention flow interception control action to prevent road network congestion from spreading. The application can integrate topographic mechanical attenuation characteristics and construct a safety warning mechanism that meets the anti-collision demand.
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Citation Information

Patent Citations

  • A Multi-Time Scale Prediction Method for Road Traffic Operation Status

    CN104778837B

  • A traffic flow prediction method

    CN112927510B