Maritime accidents in the Yangtze River: A time series analysis for 2011–2020

• Published in: Accident analysis and prevention 2023-02, Vol.180, p.106901-106901, Article 106901
• Main Authors: Sui, Zhongyi, Wen, Yuanqiao, Huang, Yamin, Song, Rongxin, Piera, Miquel Angel
• Format: Article
• Language: English
• Subjects: Accidents; Accidents, Traffic; Humans; Maritime accidents; Rivers; Safety Management; Scale-free; Ships; Small-world; Time Factors; Time series; Visibility graph; Yangtze River
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2022.106901

•The visibility graph was used to create a new bridge between maritime accident time series and complex networks.•The characteristics of the maritime accident time series were uncovered based on visibility graph.•The degree distribution of maritime accident time series network presented power-law characteristics.•The data from the Yangtze River was analyzed to reveal the dynamic features in the accident time series. The theoretical analysis of maritime accidents is a hot topic, but the time characteristics and dynamics of maritime accidents time series are still unclear. It is difficult to draw a clear conclusion from the cause analysis, so the accident is difficult to be predicted. To bridge this gap, this research analyzes the characteristics and evolution mechanism of maritime accidents time series from the perspective of complex network theory. The visual graph algorithm is used to model the complex network of maritime accidents data in 22 jurisdictions of the Yangtze River, map the time series into a complex network, and reveal the time characteristics and dynamics of maritime accidents time series based on the complex system theory. In the empirical analysis, degree distribution, clustering coefficient and network diameter are used to analyze the characteristics of time series. The results show that the degree distribution of maritime accidents time series network presents power-law characteristics in the macro and micro levels, which shows that the maritime accidents time series is scale-free. In addition, according to the clustering coefficient and network diameter, maritime accidents time series in the Yangtze River has the characteristics of small-world and hierarchical structure. The research of this manuscript shows that the occurrence of maritime accidents is not random events and does not follow specific patterns but presents the characteristics of complex systems, and this phenomenon is common. The analysis of maritime accidents time series by complex network theory can provide theoretical support for maritime traffic safety management.