[논문]COVID-19 impact on global maritime mobility

Millefiori, Leonardo M.; Braca, Paolo; Zissis, Dimitris; Spiliopoulos, Giannis; Marano, Stefano; Willett, Peter K.; Carniel, Sandro

doi:10.1038/s41598-021-97461-7

[해외논문] COVID-19 impact on global maritime mobility 원문보기

Scientific reports, v.11 no.1, 2021년, pp.18039 -

Millefiori, Leonardo M. (Research Department, NATO STO Centre for Maritime Research and Experimentation, 19126 La Spezia, Italy) , Braca, Paolo (Research Department, NATO STO Centre for Maritime Research and Experimentation, 19126 La Spezia, Italy) , Zissis, Dimitris (Department of Product and Systems Design Engineering, University of the Aegean, 84100 Syros, Greece) , Spiliopoulos, Giannis (MarineTraffic, 115 25 Athens, Greece) , Marano, Stefano (Dipartimento di Ingegneria dell’Informazione ed Elettrica e Matematica Applicata (DIEM), University of Salerno, 84084 Fisciano, SA Italy) , Willett, Peter K. (Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269 USA) , Carniel, Sandro (Research Department, NATO STO Centre for Maritime Research and Experimentation, 19126 La Spezia, Italy)

Abstract ▼ AI-Helper

To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between −5.62 and −13.77% for container ships, between +2.28 and −3.32% for dry bulk, between −0.22 and −9.27% for wet bulk, and between −19.57 and −42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.

참고문헌 (64)

1. Anderson RM Heesterbeek H Klinkenberg D Hollingsworth TD How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 2020 395 10228 931 934 10.1016/S0140-6736(20)30567-5 32164834

상세보기
2. Hellewell J Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts Lancet Glob. Health 2020 8 4 e488 e496 10.1016/S2214-109X(20)30074-7 32119825

상세보기
3. Dehning J Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions Science 2020 10.1126/science.abb9789 32414780

상세보기
4. Gaglione D Adaptive Bayesian learning and forecasting of epidemic evolution—Data analysis of the COVID-19 outbreak IEEE Access 2020 8 175244 175264 10.1109/ACCESS.2020.3019922

상세보기
5. Braca P Decision support for the quickest detection of critical COVID-19 phases Sci. Rep. 2021 11 1 13 10.1038/s41598-021-86827-6 33414495

상세보기
6. Braca P Quickest detection of COVID-19 pandemic onset IEEE Signal Process. Lett. 2021 28 683 687 10.1109/LSP.2021.3068072 34163125

상세보기
7. Soldi, G. et al. Quickest detection and forecast of pandemic outbreaks: Analysis of COVID-19 waves. In IEEE Communications Magazine (2021).
8. Crude oil WTI price data (Investing.com). https://www.investing.com/commodities/crude-oil . (Accessed 07 Sept 2020).
9. Total number of flights tracked by Flighradar24. https://www.flightradar24.com/data/statistics (Accessed 07 Sept 2020).
10. Sarkis J Cohen MJ Dewick P Schroder P A brave new world: Lessons from the COVID-19 pandemic for transitioning to sustainable supply and production Resourc. Conserv. Recycl. 2020 159 104894 10.1016/j.resconrec.2020.104894

상세보기
11. Nicola M The socio-economic implications of the coronavirus pandemic (COVID-19): A review Int. J. Surg. 2020 78 185 193 10.1016/j.ijsu.2020.04.018 32305533

상세보기
12. World Health Organization (WHO). Ambient (outdoor) air pollution. https://web.archive.org/web/20200829124448/ , https://www.who.int/news-room/fact-sheets/detail/ambient-%28outdoor%29-air-quality-and-health .
13. European Space Agency (ESA). Nitrogen dioxide concentrations over Europe. https://www.esa.int/ESA_Multimedia/Images/2020/04/Nitrogen_dioxide_concentrations_over_Europe (Accessed 26 Aug 2020).
14. World Trade Organization (WTO). Trade set to plunge as COVID-19 pandemic upends global economy. https://www.wto.org/english/news_e/pres20_e/pr855_e.htm (2020).
15. Rapaccini M Saccani N Kowalkowski C Paiola M Adrodegari F Navigating disruptive crises through service-led growth: The impact of COVID-19 on Italian manufacturing firms Ind. Market. Manag. 2020 88 225 237 10.1016/j.indmarman.2020.05.017

상세보기
16. Kelly, S. Oil price crashes into negative territory for the first time in history amid pandemic. Reuters https://reut.rs/35Pq49T (2020).
17. Sharif A Aloui C Yarovaya L COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach Int. Rev. Financ. Anal. 2020 70 101496 10.1016/j.irfa.2020.101496

상세보기
18. Guan D Global supply-chain effects of COVID-19 control measures Nat. Hum. Behav. 2020 4 6 577 587 10.1038/s41562-020-0896-8 32493967

상세보기
19. United Nations Conference on Trade and Development (UNCTAD). Review of maritime transport. https://unctad.org/en/pages/PublicationWebflyer.aspx?publicationid=1890 (2017).
20. North Atlantic Treaty Organization (NATO). Alliance Maritime Strategy. Official texts (2011).
21. Millefiori LM Braca P Bryan K Willett P Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction IEEE Trans. Aerospace Electron. Syst. 2016 52 5 1224 1245 10.1109/TAES.2016.150596

상세보기
22. United Nations Conference on Trade and Development (UNCTAD). Review of maritime transport. https://unctad.org/en/pages/PublicationWebflyer.aspx?publicationid=2563 (2019).
23. United Nations Conference on Trade and Development (UNCTAD). COVID-19 triggers marked decline in global trade. https://unctad.org/en/pages/newsdetails.aspx?OriginalVersionID=2369 (2020).
24. United Nations Conference on Trade and Development (UNCTAD). COVID-19 and maritime transport: Impact and responses. https://unctad.org/system/files/official-document/dtltlbinf2020d1_en.pdf (2020).
25. Moriarty L Public health responses to COVID-19 outbreaks on cruise ships—Worldwide, February-March 2020 Morbid. Mortal. Wkly. Rep. 2020 69 347 352 10.15585/mmwr.mm6912e3

상세보기
26. Depellegrin D Bastianini M Fadini A Menegon S The effects of COVID-19 induced lockdown measures on maritime settings of a coastal region Sci. Total Environ. 2020 740 140123 10.1016/j.scitotenv.2020.140123 32554030

상세보기
27. European Commission (EC). Coronavirus response: Support to the fishery and aquaculture sectors—fact sheet. https://web.archive.org/web/20200515234751/ , https://ec.europa.eu/fisheries/sites/fisheries/files/2020-factsheet-coronavirus-fishing-aquaculture-sectors_en.pdf (2020).
28. Viatte C Air pollution and sea pollution seen from space Surv. Geophys. 2020 10.1007/s10712-020-09599-0

상세보기
29. Melet A Earth observations for monitoring marine coastal hazards and their drivers Surv. Geophys. 2020 10.1007/s10712-020-09594-5

상세보기
30. Sardain A Sardain E Leung B Global forecasts of shipping traffic and biological invasions to 2050 Nat. Sustain. 2019 2 4 274 282 10.1038/s41893-019-0245-y

상세보기
31. International Maritime Organization (IMO). Fourth IMO GHG Study 2020 – Final report. https://docs.imo.org/Shared/Download.aspx?did=125134 (2020).
32. Shakil MH Munim ZH Tasnia M Sarowar S COVID-19 and the environment: A critical review and research agenda Sci. Total Environ. 2020 745 141022 10.1016/j.scitotenv.2020.141022 32711074

상세보기
33. Faber, J., Huigen, T. & Nelissen, D. Regulating speed: A short-term measure to reduce maritime GHG emissions. Tech. Rep., CE Delft (2017). https://web.archive.org/web/20200813172011/ , http://www.cleanshipping.org/download/Slow-steaming-CE Delft-final.pdf .
34. Hanneman, R. A. & Riddle, M. Introduction to social network methods (University of California Riverside, 2005).
35. Stopford, M. Coronavirus, climate change & smart shipping – Three maritime scenarios 2020–2050. Tech. Rep., Seatrade Maritime, part of Informa Markets (2020). https://web.archive.org/web/20210118101145/ , https://www.smm-hamburg.com/fileadmin/presse/pm sm/2020/Coronavirus__Technology___Smart_Shipping_-_Stopford_20_April_2020.pdf .
36. Cerdeiro, D. A., Komaromi, A., Liu, Y. & Saeed, M. World seaborne trade in real time: A proof of concept for building AIS-based nowcasts from scratch. https://www.imf.org/en/Publications/WP/Issues/2020/05/14/World-Seaborne-Trade-in-Real-Time-A-Proof-of-Concept-for-Building-AIS-based-Nowcasts-from-49393 (2020).
37. Saul, J. Crude oil held in sea storage hits new record at 160 million bbls. Reuters (2020). https://reut.rs/2XSijvi .
38. Falco, L., Pititto, A., Adnams, W., Earwaker, N. & Greidanus, H. EU vessel density map—detailed method. Tech. Rep., European Marine Observation and Data Network (EMODnet) (2019). https://www.emodnet-humanactivities.eu/documents/Vessel%20density%20maps_method_v1.5.pdf .
39. International Maritime Organization (IMO). UN, maritime industry, business and union leaders warn of deepening crew change crisis which threatens trade and maritime safety. https://web.archive.org/web/20210118101440/ , https://www.imo.org/en/MediaCentre/PressBriefings/Pages/32-crew-change-UNGA.aspx .
40. Sand, P. Suez canal transits rise with record tanker traffic in 2020. https://web.archive.org/web/20210118105635/ , https://www.bimco.org/news/market_analysis/2020/20200616_suez_canal_ship_transits (2020).
41. Terpilowski, S. Distance from Rotterdam to Singapore through the Cape of Good Hope about 11,720 nautical miles, compared to 8,440 nautical miles via the Suez Canal. https://bit.ly/2XPW6y7 (2020).
42. Braca P Maresca S Grasso R Bryan K Horstmann J Maritime surveillance with multiple over-the-horizon HFSW radars: An overview of recent experimentation IEEE Aerospace Electron. Syst. Mag. 2015 30 12 4 18 10.1109/MAES.2015.150004

상세보기
43. Granstrom K Natale A Braca P Ludeno G Serafino F Gamma Gaussian inverse Wishart probability hypothesis density for extended target tracking using X-band marine radar data IEEE Trans. Geosci. Remote Sens. 2015 53 12 6617 6631 10.1109/TGRS.2015.2444794

상세보기
44. Papa G Multisensor adaptive Bayesian tracking under time-varying target detection probability IEEE Trans. Aerosp. Electron. Syst. 2016 52 5 2193 2209 10.1109/TAES.2016.150522

상세보기
45. Vivone G Millefiori LM Braca P Willett P Performance assessment of vessel dynamic models for long-term prediction using heterogeneous data IEEE Trans. Geosci. Remote Sens. 2017 55 11 6533 6546 10.1109/TGRS.2017.2729622

상세보기
46. Gaglione D Bayesian information fusion and multitarget tracking for maritime situational awareness IET Radar Sonar Navig. 2020 14 12 1845 1857 10.1049/iet-rsn.2019.0508

상세보기
47. Millefiori, L. M., Braca, P. & Arcieri, G. Scalable distributed change detection and its application to maritime traffic. In 2017 IEEE International Conference on Big Data (Big Data) , 1650–1657, 10.1109/BigData.2017.8258101 (2017).
48. Coscia P Braca P Millefiori LM Palmieri F Willett P Multiple Ornstein-Uhlenbeck processes for maritime traffic graph representation IEEE Trans. Aerosp. Electron. Syst. 2018 54 5 2158 2170 10.1109/TAES.2018.2808098

상세보기
49. Tu E Zhang G Rachmawati L Rajabally E Huang G Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology IEEE Trans. Intell. Transp. Syst. 2018 19 5 1559 1582 10.1109/TITS.2017.2724551

상세보기
50. Millefiori, L., Zissis, D., Cazzanti, L. & Arcieri, G. A distributed approach to estimating sea port operational regions from lots of AIS data. In 2016 IEEE International Conference on Big Data (Big Data) , 1627–1632, 10.1109/BigData.2016.7840774 (2016).
51. Zhang L Meng Q Fwa TF Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters Transp. Res. Part E Logist. Transp. Rev. 2019 129 287 304 10.1016/j.tre.2017.07.011

상세보기
52. Vespe, M., Visentini, I., Bryan, K. & Braca, P. Unsupervised learning of maritime traffic patterns for anomaly detection. In 9th IET Data Fusion Target Tracking Conference (DF TT 2012): Algorithms Applications , 10.1049/cp.2012.0414 (2012).
53. Katsilieris, F., Braca, P. & Coraluppi, S. Detection of malicious AIS position spoofing by exploiting radar information. In Proceedings of the 16th International Conference on Information Fusion , 1196–1203 (2013).
54. d’Afflisio E Braca P Millefiori L Willett P Detecting anomalous deviations from standard maritime routes using the Ornstein-Uhlenbeck process IEEE Trans. Signal Process. 2018 66 24 6474 6487 10.1109/TSP.2018.2875887

상세보기
55. Kontopoulos I Chatzikokolakis K Tserpes K Zissis D Real-time maritime anomaly detection: Detecting intentional AIS switch-off Int. J. Big Data Intell. 2020 7 2 85 96 10.1504/IJBDI.2020.107375

상세보기
56. Ristic, B., La Scala, B., Morelande, M. & Gordon, N. Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction. In 11th International Conference on Information Fusion (2008).
57. Forti, N., Millefiori, L., Braca, P. & Willett, P. Prediction of vessel trajectories from AIS data via sequence-to-sequence recurrent neural networks. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 8936–8940, 10.1109/ICASSP40776.2020.9054421 (2020).
58. Zissis D Chatzikokolakis K Spiliopoulos G Vodas M A distributed spatial method for modeling maritime routes IEEE Access 2020 8 47556 47568 10.1109/ACCESS.2020.2979612

상세보기
59. Statista. Global merchant fleet – Number of ships by type. https://www.statista.com/statistics/264024/number-of-merchant-ships-worldwide-by-type/ (2019).
60. Millefiori, L., Braca, P., Bryan, K. & Willett, P. Adaptive filtering of imprecisely time-stamped measurements with application to AIS networks. In 18th International Conference on Information Fusion (FUSION) , 359–365 (2015).
61. Odom, P. Shipping indexes signal global economic trends. Annual Report, Globalization and Monetary Policy Institute 28–35 (2010).
62. Florescu, D. et al. Will “big data” transform official statistics? In European Conference on the Quality of Official Statistics. Vienna, Austria , 2–5 (2014). http://www.q2014.eu/fileadmin/user_upload/ESTAT-Q2014-BigDataOS-v1a.pdf .
63. Petrakos, M. et al. Analysis of the potential of selected big data repositories as data sources for official statistics. In Eurostat New Techniques and Technologies for Statistics (NTTS) Conference (2015).
64. Godsil C Royle GF Algebraic Graph Theory 2001 Springer

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[해외논문] COVID-19 impact on global maritime mobility 원문보기

Abstract ▼ AI-Helper

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