Time-series analysis of US east coast vessels based on the AIS data

Chao Wang; Bingxin Liu; Hu Liu

doi:10.1117/12.3038867

22 October 2024 Time-series analysis of US east coast vessels based on the AIS data

Chao Wang, Bingxin Liu, Hu Liu

Author Affiliations +

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 1327423 (2024) https://doi.org/10.1117/12.3038867
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

Using AIS data to mine the change characteristics of ship flow is helpful to understand the shipping industry and economic policy changes, and to provide reference for the historical events in the corresponding period. The paper selected the number of coastal ships in the east coast of the United States as the research object. The AIS data set contains about 687G of various trajectory data, with a total of about 216 million AIS trajectory data points. Through the stability test of the number of ships along the east coast of the United States, this paper makes a time series analysis of 5 types of ships from three time nodes. The results show that the number of cargo ships changes the most strongly, while other types of ships are pure random non-stable data, and there is no rule to follow. After the outbreak of COVID-19 affected the types of most ships along the east coast of the United States, resulting to the flow trough in different situations, but the cargo ships almost increased the number of other ships except other types after herd immunization, and soon reached the peak; the outbreak of Russia-Ukraine war made the number of tankers further reach the peak, which is inconsistent with the impression of European and American sanctions on Russia to reduce oil imports.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao Wang, Bingxin Liu, and Hu Liu "Time-series analysis of US east coast vessels based on the AIS data", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 1327423 (22 October 2024); https://doi.org/10.1117/12.3038867

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