Title: Construction of traffic accident knowledge graph based on the correlation analysis of risk factors

Authors: Liyan Zhang; Keyi Cao; Jian Ma; Yuan Wen; Zheng Qian; Yuchen Zhang

Addresses: School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou, 215011, China ' School of Business, Suzhou University of Science and Technology, 99 Xuefu Road, Huqiu District, Suzhou, 215009, China ' School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou, 215011, China ' School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou, 215011, China ' School of Business, Suzhou University of Science and Technology, 99 Xuefu Road, Huqiu District, Suzhou, 215009, China ' School of Civil Engineering, Suzhou University of Science and Technology, 1701 Binhe Road, New District, Suzhou, 215011, China

Abstract: With an increase of vehicles, traffic accidents have also risen. This paper presents a novel approach to create a traffic knowledge graph using a keyword extraction algorithm to analyse accident data from a specific city, focusing on identifying key terms related to the causes of accidents. The data are analysed from four aspects: human factors, vehicles, road conditions and environmental factors, to construct the knowledge graph. TextRank is a graph-based unsupervised keyword extraction method that ranks words based on their cooccurrence in a sliding window. The findings indicate that the improved TextRank algorithm, which incorporates word vectors and a multi-feature weighting mechanism, outperforms traditional TextRank and inverse document frequency methods in keyword extraction. The present TextRank algorithm effectively combines word-specific attributes and structural features, delivering better extraction performance.

Keywords: traffic accidents; knowledge graph; TextRank algorithm; keyword extraction.

DOI: 10.1504/IJVSMT.2026.152622

International Journal of Vehicle Systems Modelling and Testing, 2026 Vol.20 No.1, pp.1 - 24

Received: 10 Feb 2025
Accepted: 27 Apr 2025
Published online: 31 Mar 2026 *

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