Hazards correlation analysis of railway accidents: A real-world case study based on the decade-long UK railway accident data.

• This paper proposes a hazards correlation analysis method for railway accidents. • The knowledge graph theory and big data techniques are applied into the method. • Real-world case studies based on the decade-long UK railway accident data are conducted. With the continuous development and construction of railway transportation, the railway accidents occur frequently which greatly threatens the life safety of passengers and the further development of railway industry. Discussing and summarizing experience from past accidents is benefit to improve the safety of railway. This paper proposes a modeling method for the correlation analysis of hazards in railway accidents based on the knowledge graph theory. By describing the association between accidents and hazards in the knowledge graph network, the potential law of accident occurrence is revealed. The innovation of this study is that it considers the correlations between hazards. In addition, the hazards are further refined, and new topology indexes that adapt to the heterogeneous structure characteristics of knowledge graph are presented. Based on actual railway accident data in the UK, a number of key hazards have been identified using the methods proposed in this paper. The experimental results show that by controlling key hazards one by one, the harmful consequence caused by hazards to accidents is also continuously decreasing. Finally, based on the experimental exploration of the correlation between key hazards, corresponding preventive measures were developed. The method based on knowledge graph is expected to be applied to explore the relationship between hazards in railway accidents and provide additional decision-making information for the prevention of railway accidents. [ABSTRACT FROM AUTHOR]