Abstract
The global oil tanker shipping network emerges from individual ship and fleet decisions driven by economic, environmental, and operational factors. However, most existing shipping network analysis rely on static, time-aggregated representations, overlooking critical temporality connecting individual vessel routing strategies with both operational efficiency and global cargo flows. To address this gap, we introduce a dual-scale framework complementing sequential motif analysis—capturing recurrent patterns in vessel movement sequences—with Dynamic Mode Decomposition (DMD), extracting temporal dynamics from vessel trajectories to global cargo flows. Using tanker movement data across four vessel classes, we demonstrate that vessels exhibiting diverse regional exploration patterns spend up to 50% more time transporting rather than seeking cargo, indicating greater economic and environmental efficiency. At the system scale, DMD analysis reveals distinct seasonality with an average peak-to-trough amplitude of 16%. Major import regions show synchronous annual demand cycles, while export regions exhibit anti-synchronicity. These temporal patterns, invisible to static analysis, reveal performance differences that enable route optimization for both economic and environmental benefits.
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Data availability
The vessel voyage data that support the findings of this study are available from AlphaOcean, but restrictions apply to the availability of these data, which were used under license for the current study due to commercial confidentiality agreements, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of AlphaOcean (https://alphaocean.ai/). Map figures were generated with data from the coastal data by GSHHG.
Code availability
No substantial custom code was developed for this study. Sequential motif analysis was performed using the RandomWalker package (accessible via https://github.com/narnolddd/randomwalker). Dynamic mode decomposition was performed using PyDMD version 1.0.0, and singular spectrum analysis was conducted using py_ssa_lib version 0.0.1. Statistical analysis was performed in Python version 3.9.19 using standard Python libraries and packages. All libraries are freely available and can be installed via standard Python package managers.
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Acknowledgements
K.T. acknowledges the PhD studentship support from Northeastern University.
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K.T., N.A., M.C., M.I., and I.Z.K. designed research; K.T. and N.A. performed research; M.S. contributed analytic tools; K.T., N.A., A.H., M.C., M.S., and I.Z.K. analyzed data; K.T., N.A., A.H., M.C., M.S., and I.Z.K. wrote the paper.
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K.T., N.A., A.H., and M.S. declare no competing interests. M.I. and M.C. are employees of a shipping-related company, AlphaOcean.ai. M.I., M.C., and I.Z.K. own shares in AlphaOcean.ai. The start-up company’s core product is building optimization models to assist shipowners with refueling strategies, which are not related to the research in this paper.
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Teo, K., Arnold, N., Hone, A. et al. Unveiling individual and collective temporal patterns in the tanker shipping network. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70013-1
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DOI: https://doi.org/10.1038/s41467-026-70013-1
