Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression: A case study in the Bohai Sea, China

Yongtian Shen; Zhe Zeng; Dan Liu; Pei Du

doi:10.1007/s13131-022-2137-7

Volume 42 Issue 12

Dec. 2023

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Article Navigation > Acta Oceanologica Sinica > 2023 > 42(12): 77-89

Yongtian Shen, Zhe Zeng, Dan Liu, Pei Du. Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression: A case study in the Bohai Sea, China[J]. Acta Oceanologica Sinica, 2023, 42(12): 77-89. doi: 10.1007/s13131-022-2137-7

Citation:

Yongtian Shen, Zhe Zeng, Dan Liu, Pei Du. Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression: A case study in the Bohai Sea, China[J]. Acta Oceanologica Sinica, 2023, 42(12): 77-89. doi: 10.1007/s13131-022-2137-7

Citation:

Yongtian Shen, Zhe Zeng, Dan Liu, Pei Du. Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression: A case study in the Bohai Sea, China[J]. Acta Oceanologica Sinica, 2023, 42(12): 77-89. doi: 10.1007/s13131-022-2137-7

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Exploring spatial non-stationarity of near-miss ship collisions from AIS data under the influence of sea fog using geographically weighted regression: A case study in the Bohai Sea, China

doi: 10.1007/s13131-022-2137-7

Yongtian Shen^{1, 2},
Zhe Zeng^{1
,
,},
Dan Liu¹,
Pei Du^{1, 3}

1.
China University of Petroleum, Qingdao 266580, China
2.
Anhui Provincial Geomatic Center, Hefei 230031, China
3.
Nanjing Normal University School of Geography, Nanjing 210023, China

More Information

Corresponding author: E-mail: zhe.zeng.79@gmail.com
Received Date: 2022-10-12
Accepted Date: 2022-12-19

Available Online: 2023-06-05

Publish Date: 2023-12-25

Abstract

Abstract

Sea fog is a disastrous weather phenomenon, posing a risk to the safety of maritime transportation. Dense sea fogs reduce visibility at sea and have frequently caused ship collisions. This study used a geographically weighted regression (GWR) model to explore the spatial non-stationarity of near-miss collision risk, as detected by a vessel conflict ranking operator (VCRO) model from automatic identification system (AIS) data under the influence of sea fog in the Bohai Sea. Sea fog was identified by a machine learning method that was derived from Himawari-8 satellite data. The spatial distributions of near-miss collision risk, sea fog, and the parameters of GWR were mapped. The results showed that sea fog and near-miss collision risk have specific spatial distribution patterns in the Bohai Sea, in which near-miss collision risk in the fog season is significantly higher than that outside the fog season, especially in the northeast (the sea area near Yingkou Port and Bayuquan Port) and the southeast (the sea area near Yantai Port). GWR outputs further indicated a significant correlation between near-miss collision risk and sea fog in fog season, with higher R-squared (0.890 in fog season, 2018), than outside the fog season (0.723 in non-fog season, 2018). GWR results revealed spatial non-stationarity in the relationships between-near miss collision risk and sea fog and that the significance of these relationships varied locally. Dividing the specific navigation area made it possible to verify that sea fog has a positive impact on near-miss collision risk.
- near-miss,
- sea fog,
- geographically weighted regression,
- automatic identification system (AIS)

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References(41)

References

Brunsdon C, Fotheringham A S, Charlton M E. 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4): 281–298

Burnett A. 1996. A Guide to the Collision Avoidance Rules (Fifth edition). In: Cockcroft A N, Lameijer J N F. Butterworth–Heinemann, Oxford: The Journal of Navigation, 49(3): 455–456

Cockcroft A N, Lameijer J N F. 1996. A Guide to the Collision Avoidance Rules. 5th ed. Oxford: Butterworth-Heinemann Press, 455–456

Celik M, Lavasani S M, Wang Jin. 2010. A risk-based modelling approach to enhance shipping accident investigation. Safety Science, 48(1): 18–27. doi: 10.1016/j.ssci.2009.04.007

Chen Pengfei, Huang Yamin, Mou Junmin, et al. 2019. Probabilistic risk analysis for ship-ship collision: state-of-the-art. Safety Science, 117: 108–122. doi: 10.1016/j.ssci.2019.04.014

Cho Y K, Kim M O, Kim B C. 2000. Sea fog around the Korean peninsula. Journal of Applied Meteorology, 39(12): 2473–2479. doi: 10.1175/1520-0450(2000)039<2473:SFATKP>2.0.CO;2

Du Pei, Zeng Zhe, Zhang Jingwei, et al. 2021. Fog season risk assessment for maritime transportation systems exploiting Himawari-8 data: a case study in Bohai Sea, China. Remote Sensing, 13(17): 3530. doi: 10.3390/rs13173530

Eyre J R, Brownscombe J L, Allam R J, et al. 1984. Detection of fog at night using Advanced Very High Resolution Radiometer (AVHRR) imagery. Meteorological Magazine, 113(1346): 266–271

Fang Zhixiang, Yu Hongchu, Ke Ranxuan, et al. 2019. Automatic identification system-based approach for assessing the near-miss collision risk dynamics of ships in ports. IEEE Transactions on Intelligent Transportation Systems, 20(2): 534–543. doi: 10.1109/TITS.2018.2816122

Fotheringham A S, Yang Wenbai, Kang Wei. 2017. Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6): 1247–1265. doi: 10.1080/24694452.2017.1352480

Gao Yuan, Jiang Guoyou. 2018. Research on influencing factors and countermeasures of fog navigation in Weihai Harbour. In: Proceedings of the 2018 5th International Conference on Education, Management, Arts, Economics and Social Science. Sanya: Atlantis Press, 1081–1083

Gluver H. 2017. Ship Collision Analysis: Proceedings of the International Symposium on Advances in Ship Collision Analysis, Copenhagen, Denmark, 10−13 May 1998. London, UK: Routledge

Hao Zengzhou, Pan Delu, Gong Fang, et al. 2009. Sea fog characteristics based on MODIS data and streamer model. In: Proceedings of SPIE 7475, Remote Sensing of Clouds and the Atmosphere XIV. Berlin: SPIE, 312–319

Harel O. 2009. The estimation of R² and adjusted R² in incomplete data sets using multiple imputation. Journal of Applied Statistics, 36(10): 1109–1118. doi: 10.1080/02664760802553000

Hazell E C, Rinner C. 2020. The impact of spatial scale: exploring urban butterfly abundance and richness patterns using multi-criteria decision analysis and principal component analysis. International Journal of Geographical Information Science, 34(8): 1648–1681. doi: 10.1080/13658816.2019.1675072

Heo K Y, Park S, Ha K J, et al. 2014. Algorithm for sea fog monitoring with the use of information technologies. Meteorological Applications, 21(2): 350–359. doi: 10.1002/met.1344

International Maritime Organization Maritime Safety Committee. 1998. Recommendation on performance standards for a universal shipborne automatic identification system (AIS), Resolution MSC.74(69). https://wwwcdn.imo.org/localresources/en/OurWork/Safety/Documents/AIS/Resolution%20MSC.74(69).pdf [1998-05-12/2022-10-01]

Juszkiewicz W. 2016. Verification of the accuracy requirements for relative course and closest point of approach. Scientific Journals Maritime University of Szczecin, 45(117): 108–113

Karahalios H. 2014. The contribution of risk management in ship management: the case of ship collision. Safety Science, 63: 104–114. doi: 10.1016/j.ssci.2013.11.004

Kim K I, Jeong J S, Lee B G. 2017. Study on the analysis of near-miss ship collisions using logistic regression. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21(3): 467–473. doi: 10.20965/jaciii.2017.p0467

Lewison G R G. 1980. The estimation of collision risk for marine traffic in UK waters. The Journal of Navigation, 33(3): 317–328. doi: 10.1017/S037346330004073X

Lu Binbin, Brunsdon C, Charlton M, et al. 2017. Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5): 982–998. doi: 10.1080/13658816.2016.1263731

Maritime Safety Administration of the People’s Republic of China. 2018. Sailing Direction on Chinese Coast: North Area. Beijing: China Communications Press Co., Ltd., 27–198

Moran P A P. 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society: Series B (Methodological), 10(2): 243–251. doi: 10.1111/j.2517-6161.1948.tb00012.x

Pietrzykowski Z, Uriasz J. 2009. The ship domain—a criterion of navigational safety assessment in an open sea area. The Journal of Navigation, 62(1): 93–108. doi: 10.1017/S0373463308005018

Ryu Han-Sol, Hong S. 2020. Sea fog detection based on Normalized Difference Snow Index using advanced Himawari imager observations. Remote Sensing, 12(9): 1521. doi: 10.3390/rs12091521

Rømer H, Petersen H J S, Haastrup P. 1995. Marine accident frequencies—review and recent empirical results. The Journal of Navigation, 48(3): 410–424. doi: 10.1017/S037346330001290X

Store R, Jokimäki J. 2003. A GIS-based multi-scale approach to habitat suitability modeling. Ecological Modelling, 169(1): 1–15. doi: 10.1016/S0304-3800(03)00203-5

Szlapczynski R, Szlapczynska J. 2021. A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems. Reliability Engineering & System Safety, 214: 107766

United Nations Conference on Trade and Development. 2017. Review of Maritime Transport 2017. Switzerland: United Nations

van Iperen W H. 2015. Classifying ship encounters to monitor traffic safety on the North Sea from AIS Data. TransNav: the International Journal on Marine Navigation and Safety of Sea Transportation, 9(1): 51–58. doi: 10.12716/1001.09.01.06

Wu Xing, Aesha L. Mehta, Victor A. Zaloom, et al 2016. Analysis of waterway transportation in Southeast Texas waterway based on AIS data. Ocean Engineering, 121: 196–209. doi: 10.1016/j.oceaneng.2016.05.012

Wu Xiaojing, Li Sanmei. 2014. Automatic sea fog detection over Chinese adjacent oceans using Terra/MODIS data. International Journal of Remote Sensing, 35(21): 7430–7457. doi: 10.1080/01431161.2014.968685

Wu Xiaojing, Li Sanmei, Liao Mi, et al. 2015. Analyses of seasonal feature of sea fog over the Yellow Sea and Bohai Sea based on the recent 20 years of satellite remote sensing data. Haiyang Xuebao (in Chinese), 37(1): 63–72

Wu Dong, Lu Bo, Zhang Tianche, et al. 2015. A method of detecting sea fogs using CALIOP data and its application to improve MODIS-based sea fog detection. Journal of Quantitative Spectroscopy and Radiative Transfer, 153: 88–94. doi: 10.1016/j.jqsrt.2014.09.021

Xiao Yanfang, Zhang Jie, Qin Ping. 2019. An algorithm for daytime sea fog detection over the Greenland Sea based on MODIS and CALIOP data. Journal of Coastal Research, 90(sp1): 95–103. doi: 10.2112/SI90-012.1

Xu Mengqiu, Wu Ming, Guo Jun, et al. 2022. Sea fog detection based on unsupervised domain adaptation. Chinese Journal of Aeronautics, 35(4): 415–425. doi: 10.1016/j.cja.2021.06.019

Yoo S L. 2018. Near-miss density map for safe navigation of ships. Ocean Engineering, 163: 15–21. doi: 10.1016/j.oceaneng.2018.05.065

Zhang Weibin, Goerlandt F, Kujala P, et al. 2016. An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124: 141–156. doi: 10.1016/j.oceaneng.2016.07.059

Zhang Weibin, Goerlandt F, Montewka J, et al. 2015. A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107: 60–69. doi: 10.1016/j.oceaneng.2015.07.046

Zhang Weibin, Kopca C, Tang Jinjun, et al. 2017. A systematic approach for collision risk analysis based on AIS data. The Journal of Navigation, 70(5): 1117–1132. doi: 10.1017/S0373463317000212

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