Skip to main content

Using big data to track marine oil transportation along the 21st-century Maritime Silk Road

Abstract

China’s designation of the “21st-century Maritime Silk Road” (MSR) region is of extraordinary significance to its maritime rights, transportation security, and socio-economic development. We developed a technical framework allowing the use of “big data” derived from the Automatic Identification System (AIS, an automatic ship-tracking network) for two purposes: the accurate mapping of oil tanker trajectories and the creation of heat maps showing the relative use of oil tanker routes and marine shipping chokepoints. We then applied these methods to 1.5 billion AIS records collected within the MSR in 2014 to statistically identify and analyze busy routes, areas, and chokepoints in this strategic region. Our results demonstrate that the proposed framework can provide an effective analysis of oil movements based on large-scale AIS datasets, helping researchers and policy makers better understand the footprint and strategic implications of maritime oil transportation in the MSR region.

This is a preview of subscription content, access via your institution.

References

  1. Jia H. Scientific collaborations shine on Belt and Road. Natl Sci Rev, 2017, 4: 652–657

    Article  Google Scholar 

  2. Zhang Z X. China’s energy security, the Malacca dilemma and responses. Energy Policy, 2011, 39: 7612–7615

    Article  Google Scholar 

  3. Song C, Li C. Relationship between chinese and international crude oil prices: A vec-tarch approach. Math Problems Eng, 2015, 2015: 1–10

    Google Scholar 

  4. Brussaard C P D, Peperzak L, Beggah S, et al. Immediate ecotoxicological effects of short-lived oil spills on marine biota. Nat Commun, 2016, 7: 11206

    Article  Google Scholar 

  5. Feng J, Chen H, Bi F K, et al. Detection of oil spills in a complex scene of SAR imagery. Sci China Technol Sci, 2014, 57: 2204–2209

    Article  Google Scholar 

  6. Guo J, Liu X, Xie Q. Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem. Chin Sci Bull, 2013, 58: 2276–2281

    Article  Google Scholar 

  7. Cheng L, Duran M A. Logistics for world-wide crude oil transportation using discrete event simulation and optimal control. Comput Chem Eng, 2004, 28: 897–911

    Article  Google Scholar 

  8. Walls W D. Petroleum refining industry in China. Energy Policy, 2010, 38: 2110–2115

    Article  Google Scholar 

  9. Chu C, Chu F, Zhou M C, et al. A polynomial dynamic programming algorithm for crude oil transportation planning. IEEE Trans Automat Sci Eng, 2012, 9: 42–55

    Article  Google Scholar 

  10. Cervera M A, Ginesi A, Eckstein K. Satellite-based vessel Automatic Identification System: A feasibility and performance analysis. Int J Satell Commun Network, 2011, 29: 117–142

    Article  Google Scholar 

  11. McCauley D J, Woods P, Sullivan B, et al. Ending hide and seek at sea. Science, 2016, 351: 1148–1150

    Article  Google Scholar 

  12. Doulkeridis C, George A. V, Qu Q, et al. Mobility Analytics for Spatio-Temporal and Social Data: First International Workshop. Munich: Springer Press, 2017. 28–31

    Google Scholar 

  13. Kroodsma D A, Mayorga J, Hochberg T, et al. Tracking the global footprint of fisheries. Science, 2018, 359: 904–908

    Article  Google Scholar 

  14. Lazer D, Kennedy R, King G, et al. The parable of Google Flu: Traps in big data analysis. Science, 2014, 343: 1203–1205

    Article  Google Scholar 

  15. Etienne L, Devogele T, Buchin M, et al. Trajectory Box Plot: A new pattern to summarize movements. Int J Geographical Inf Sci, 2016, 30: 835–853

    Article  Google Scholar 

  16. Demšar U, Virrantaus K. Space-time density of trajectories: Exploring spatio-temporal patterns in movement data. Int J Geographical Inf Sci, 2010, 24: 1527–1542

    Article  Google Scholar 

  17. de Souza E N, Boerder K, Matwin S, et al. Improving fishing pattern detection from satellite ais using data mining and machine learning. PLoS ONE, 2016, 11: e0158248

    Article  Google Scholar 

  18. Pallotta G, Vespe M, Bryan K. Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 2013, 15: 2218–2245

    Article  MATH  Google Scholar 

  19. Mascaro S, Nicholso A E, Korb K B. Anomaly detection in vessel tracks using Bayesian networks. Int J Approximate Reasoning, 2014, 55: 84–98

    Article  Google Scholar 

  20. Lei P R. A framework for anomaly detection in maritime trajectory behavior. Knowl Inf Syst, 2016, 47: 189–214

    Article  Google Scholar 

  21. Silveira P A M, Teixeira A P, Soares C G. Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. J Navigation, 2013, 66: 879–898

    Article  Google Scholar 

  22. Zhang W, Goerlandt F, Montewka J, et al. A method for detecting possible near miss ship collisions from AIS data. Ocean Eng, 2015, 107: 60–69

    Article  Google Scholar 

  23. Zhang W, Goerlandt F, Kujala P, et al. An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Eng, 2016, 124: 141–156

    Article  Google Scholar 

  24. Kaluza P, Kölzsch A, Gastner M T, et al. The complex network of global cargo ship movements. J R Soc Interface, 2010, 7: 1093–1103

    Article  Google Scholar 

  25. Vettor R, Guedes Soares C. Detection and analysis of the main routes of voluntary observing ships in the North Atlantic. J Navigation, 2015, 68: 397–410

    Article  Google Scholar 

  26. Wen Y T, Lai C H, Lei P R, et al. Routeminer: Mining ship routes from a massive maritime trajectories. In: Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management (MDM). Pittsburgh: IEEE, 2014. 353–356

    Chapter  Google Scholar 

  27. Lei P R, Tsai T H, Peng W C. Discovering maritime traffic route from AIS network. In: Proceedings of the 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS). Kanazawa: IEEE, 2016. 1–6

    Google Scholar 

  28. Zhen R, Jin Y, Hu Q, et al. Maritime anomaly detection within coastal waters based on vessel trajectory clustering and naïve bayes classifier. J Navigation, 2017, 70: 648–670

    Article  Google Scholar 

  29. Lee J G, Han J, Whang K Y. Trajectory clustering: a partition-andgroup framework. In: Proceedings of the 2007 ACM SIGMOD international conference on Management of data. Beijing: ACM, 2007. 593–604

    Chapter  Google Scholar 

  30. Liu B, de Souza E N, Matwin S, et al. Knowledge-based clustering of ship trajectories using density-based approach. In: Proceedings of the 2014 IEEE International Conference on Big Data (Big Data). Washington DC: IEEE, 2014. 603–608

    Chapter  Google Scholar 

  31. Yan W, Wen R, Zhang A N, et al. Vessel movement analysis and pattern discovery using density-based clustering approach. In: Proceedings of the 2016 IEEE International Conference on Big Data (Big Data). Washington DC: IEEE, 2016. 3798–3806

    Chapter  Google Scholar 

  32. Pallotta G, Vespe M, Bryan K. Traffic knowledge discovery from ais data. In: Proceedings of the 2013 16th International Conference on Information Fusion (FUSION). Istanbul: IEEE, 2013. 1996–2003

    Google Scholar 

  33. Chen J, Lu F, Peng G. A quantitative approach for delineating principal fairways of ship passages through a strait. Ocean Eng, 2015, 103: 188–197

    Article  Google Scholar 

  34. Qu X, Meng Q, Suyi L. Ship collision risk assessment for the Singapore Strait. Accident Anal Prevention, 2011, 43: 2030–2036

    Article  Google Scholar 

  35. Boerder K, Miller N A, Worm B. Global hot spots of transshipment of fish catch at sea. Sci Adv, 2018, 4: eaat7159

    Article  Google Scholar 

  36. Cózar A, Martí E, Duarte C M, et al. The Arctic Ocean as a dead end for floating plastics in the North Atlantic branch of the Thermohaline Circulation. Sci Adv, 2017, 3: e1600582

    Article  Google Scholar 

  37. Wu X D, Zhu X Q, Wu G Q, et al. Data mining with big data. IEEE Trans Knowl Data Eng, 2014, 26: 97–107

    Article  Google Scholar 

  38. Yin Y F, Gong G H, Han L. Theory and techniques of data mining in CGF behavior modeling. Sci China Inf Sci, 2011, 54: 717–731

    Article  Google Scholar 

  39. Zhu J, Chen J, Hu W, et al. Big learning with bayesian methods. Natl Sci Rev, 2017, 4: 627–651

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to FangLi Zhang or ManChun Li.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cheng, L., Yan, Z., Xiao, Y. et al. Using big data to track marine oil transportation along the 21st-century Maritime Silk Road. Sci. China Technol. Sci. 62, 677–686 (2019). https://doi.org/10.1007/s11431-018-9335-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-018-9335-1

Keywords

  • 21st-century Maritime Silk Road (MSR)
  • maritime oil transportation
  • transportation chokepoints
  • heat map
  • automatic identification system (AIS)