Knowledge modeling and intelligent analysis of road traffic accidents

Xiaojia Liu; Yunjie Chen; Wanli Peng; Dexin Yu

doi:10.1117/12.3039587

28 August 2024 Knowledge modeling and intelligent analysis of road traffic accidents

Xiaojia Liu, Yunjie Chen, Wanli Peng, Dexin Yu

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 132515U (2024) https://doi.org/10.1117/12.3039587
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

This paper proposes a method for efficiently organizing, managing, and visualizing road traffic accident data through knowledge graph technology. It begins by outlining the construction process of the road traffic accident knowledge graph based on the logical and technical architecture of knowledge graph. Then, it performs knowledge modeling and extraction using road traffic safety guidelines and accident data, storing the extracted knowledge in a Neo4j graph database. Finally, it conducts accident portrait, classification, statistics, and correlation analysis using the constructed knowledge graph. The research demonstrates that knowledge graph technology facilitates rapid querying and analysis of accident data, visualizes accident knowledge, and identifies associations between accident elements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaojia Liu, Yunjie Chen, Wanli Peng, and Dexin Yu "Knowledge modeling and intelligent analysis of road traffic accidents", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 132515U (28 August 2024); https://doi.org/10.1117/12.3039587

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