A big data analytics method for the evaluation of maritime traffic safety using automatic identification system data

Quandang Ma, Huan Tang, Cong Liu*, Mingyang Zhang, Dingze Zhang, Zhao Liu, Liye Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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The complex traffic situations are among the factors influencing maritime safety. They can be quantitatively estimated through the analysis of traffic data. This paper explores the impact of complex traffic situations on maritime safety, focusing on inland waterway traffic. It presents a big data analytics method, utilizing data from the Automatic Identification System (AIS) and historical maritime accident records. The methodology involves AIS data preprocessing and spatial autocorrelation models, including Moran's index, to extract and evaluate the dynamic characteristics of maritime traffic. The analysis of traffic characteristic includes a thorough investigation into the spatial-temporal distribution of ship average speed and trajectory density. The paper then introduces an effective traffic characteristic analysis model that evaluates the relationship between maritime traffic patterns and accidents. The study, specifically targeting the Nanjing section of the Yangtze River, reveals variations in ship trajectory density and average speed over time. It identifies several hotspots with a significant local correlation between these factors. Moreover, a substantial correlation is found between the locations of maritime accidents and areas with increased ship trajectory density and average speed. These results may provide insights for traffic safety management and highlight strategies for preventing maritime accidents.

Original languageEnglish
Article number107077
Number of pages21
Publication statusPublished - 1 May 2024
MoE publication typeA1 Journal article-refereed


  • Big-data analytics
  • Maritime accidents
  • Maritime traffic safety
  • Spatial-temporal analysis
  • Yangtze river


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