A method for constructing a traffic safety knowledge graph of a continuous longitudinal slope road section of a highway

By constructing a multi-level knowledge graph, integrating multi-source heterogeneous data, and using Bayesian networks for dynamic risk assessment, the problems of insufficient data integration and low prediction accuracy in traditional methods are solved, and high-precision real-time risk management of continuous longitudinal slope sections of highways is realized.

CN120258122BActive Publication Date: 2026-03-27GANSU PROVINCE TRANSPORTATION PLANNING SURVEY & DESIGN INST +1
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Patent Information

Application Number
CN202510526795.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-04-25
Publication Date
2026-03-27
Estimated Expiration
2045-04-25

AI Technical Summary

Technical Problem

Traditional traffic safety analysis methods are insufficient in integrating multi-source heterogeneous data in highway sections with continuous longitudinal slopes, lack risk assessment of the coupling between dynamic environmental factors and static road section characteristics, and rely on fixed thresholds for real-time early warning, resulting in low prediction accuracy.

Method used

A multi-level knowledge graph is constructed, including a static topology layer, a dynamic attribute layer, and a probabilistic reasoning layer. It integrates heterogeneous data from multiple sources, uses Bayesian networks for dynamic risk assessment, and achieves real-time risk warning through a graph database.

Benefits of technology

It has achieved high-precision risk assessment and real-time early warning, and improved the decision support capability for traffic safety management under complex terrain conditions.

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Abstract

The application belongs to the cross field of traffic engineering and information technology, and discloses a highway continuous longitudinal slope section traffic safety knowledge graph construction method.The method integrates section characteristics, traffic flow, historical accidents and environmental data, and constructs a multi-source data set through cleaning and standardization processing; defines section, accident, environment and vehicle entities, and constructs a multi-level knowledge graph containing a static topology layer, a dynamic attribute layer and a probability reasoning layer; uses natural language processing technology to extract knowledge triples, combines with a rule engine to dynamically fuse the correlation logic of slope, environment and accident data, realizes real-time risk assessment and early warning; uses a graph database to store the graph, supports semantic query and dynamic update.The application breaks through the limitations of traditional single data source analysis, solves the coupling risk assessment problem of dynamic environmental factors and static characteristics under complex terrain through multi-source fusion and probability reasoning mechanism, and significantly improves the traffic safety risk prediction accuracy.
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