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Analysis of global marine oil trade based on automatic identification system (AIS) data

Author

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  • Yan, Zhaojin
  • Xiao, Yijia
  • Cheng, Liang
  • Chen, Song
  • Zhou, Xiao
  • Ruan, Xiaoguang
  • Li, Manchun
  • He, Rong
  • Ran, Bin

Abstract

A fine-grained analysis framework of global marine oil trade based on AIS data is developed to address the existing problems of using statistical data to analyze oil trade without sufficient temporal and spatial resolution. The framework includes three modules: traffic route analysis, trade volume analysis, and trade network analysis. A ship cargo payload calculation (SCPC) model is proposed to take the draught, shape, and size of the vessel and seawater density into consideration. It calculates the oil trade volume of each oil tanker voyage as a unit. More than 3.4 billion global automatic identification system (AIS) records in 2017 are utilized to verify the proposed framework and achieve the following findings. The Middle East-Strait of Malacca-East Asia oil transport route is the busiest and largest trade volume route in the global marine oil trade. The oil trade volume of the world's top 20 oil-importing and oil-exporting countries calculated based on AIS data is strongly correlated to the Joint Organizations Data Initiative (JODI) statistics with the determination coefficient (R2) of 0.8798. More than 90% of the world's top 20 oil-importing and oil-exporting countries have more than five oil trading partners. The experimental results show that the proposed analysis framework has utilized the most minimal research object, every oil tanker's trajectory, to realize the fine-grained research of marine oil trade based on oil tanker flows analysis. The derived oil flows with directions and trade volumes provide the basis for constructing a directed weighted oil trade network.

Suggested Citation

  • Yan, Zhaojin & Xiao, Yijia & Cheng, Liang & Chen, Song & Zhou, Xiao & Ruan, Xiaoguang & Li, Manchun & He, Rong & Ran, Bin, 2020. "Analysis of global marine oil trade based on automatic identification system (AIS) data," Journal of Transport Geography, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:jotrge:v:83:y:2020:i:c:s0966692319306076
    DOI: 10.1016/j.jtrangeo.2020.102637
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