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Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics

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  • Rong, H.
  • Teixeira, A.P.
  • Guedes Soares, C.

Abstract

A spatial correlation analysis of near collision clusters with local traffic characteristics is presented. The Moran's I and Getis-Ord Gi* spatial autocorrelation methods are used to determine whether near collisions show spatial clustering from global and local perspectives. The application of the developed approach to Automatic Identification System data of the maritime traffic off the coast of Portugal shows that there are several hotspots where the density of ship near collisions is relatively high. A co-occurrence analysis is then conducted to relate the near collision hotspots with selected local maritime traffic characteristics such as the average ship speed, speed dispersion, degree of speed acceleration, ship route overlaps and degree of angular deviation from ship route centreline. The identification of near collisions clusters and the assessment of the correlation of ship near collision hotspots with the maritime traffic features provide a means for improving maritime safety and reducing the occurrence of ship-ship collisions.

Suggested Citation

  • Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2021. "Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000314
    DOI: 10.1016/j.ress.2021.107463
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    Cited by:

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    3. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Gao, Dawei & Zhu, Yongsheng & Guedes Soares, C., 2023. "Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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    6. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Antão, P. & Sun, S. & Teixeira, A.P. & Guedes Soares, C., 2023. "Quantitative assessment of ship collision risk influencing factors from worldwide accident and fleet data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    9. Silveira, P. & Teixeira, A.P. & Figueira, J.R. & Guedes Soares, C., 2021. "A multicriteria outranking approach for ship collision risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Du, Lei & Banda, Osiris A. Valdez & Huang, Yamin & Goerlandt, Floris & Kujala, Pentti & Zhang, Weibin, 2021. "An empirical ship domain based on evasive maneuver and perceived collision risk," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

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