Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis

Taha Talip Türkistanlı

doi:10.33714/masteb.1855338

Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis

Abstract

While the International Regulations for Preventing Collisions at Sea (COLREG) establish universal design and operational standards to ensure maritime safety, the practical implementation of legal flexibility through exemptions and equivalents remains a critical yet under-examined aspect of regulatory compliance for modern and specialized vessels. This study investigates how these mechanisms are operationalized by analyzing official notifications submitted by national administrations to the International Maritime Organization (IMO). A structured dataset of 1,281 verified COLREG-related cases was extracted from 12,834 notifications spanning 2012 to 2025 using a hybrid pattern-mining and text-normalization method. The data were then analyzed using a methodological framework comprising association rule mining, network analysis, and statistical tests to uncover structural, relational, and temporal patterns. Findings reveal that notifications concentrate within a few frequently coupled rule sets, with issues varying systematically by vessel type and over time. Rather than isolating individual clauses, the evidence points to coordinated adjustments reflecting practical design constraints, administrative practices, and evolving technologies. The resulting data-driven framework exposes structural regularities in vessel design conflicts, providing insights to inform survey practices, support consistent decision-making, and facilitate future policy development.

Keywords

References

Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216. https://doi.org/10.1145/170036.170072
Bateman, S. (2015). The role of flag states. In R. Warner & S. Kaye (Eds.), Routledge handbook of maritime regulation and enforcement (pp. 43–58). Routledge.
Bi, Q., Wang, M., Huang, Y., Lai, M., Liu, Z., & Bi, X. (2023). Ship collision avoidance navigation signal recognition via vision sensing and machine forecasting. IEEE Transactions on Intelligent Transportation Systems, 24(11), 11743-11755. https://doi.org/10.1109/TITS.2023.3287709
Bukaty, V. M., & Morozova, S. U. (2009). On determination of the head-on situation under Rule 14 of COLREG-72. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 4(4), 303-308.
Çakır, E., Fışkın, R., & Sevgili, C. (2021). Investigation of tugboat accidents severity: An application of association rule mining algorithms. Reliability Engineering & System Safety, 209, 107470. https://doi.org/10.1016/j.ress.2021.107470
Chang, C.-H., Wijeratne, I. B., Kontovas, C., & Yang, Z. (2024). COLREG and MASS: Analytical review to identify research trends and gaps in the Development of Autonomous Collision Avoidance. Ocean Engineering, 302, 117652. https://doi.org/10.1016/j.oceaneng.2024.117652
ClassNK. (1996). Positioning of Sidelights on Hong Kong Flag Container Ships. ClassNK Technical Information.
Crosbie, J. W. (2006). Lookout versus lights: Some sidelights on the dark history of navigation lights. The Journal of Navigation, 59(1), 1-7. https://doi.org/10.1017/S0373463305003607

Dalgic, Y., Lazakis, I., & Turan, O. (2015). Investigation of optimum crew transfer vessel fleet for offshore wind farm maintenance operations. Wind Engineering, 39(1), 31-52. https://doi.org/10.1260/0309-524X.39.1.31
DNV, Det Norske Veritas. (2018). Autonomous and remotely operated ships. Class guideline-DNVGL-CG-0264.
Dong, W., Zhang, P., & Li, J. (2023). Safety first—A critical examination of the lights and shapes in COLREGs. Journal of Marine Science and Engineering, 11(8), 1508. https://doi.org/10.3390/jmse11081508
Fan, L., Luo, M., & Yin, J. (2014). Flag choice and Port State Control inspections—Empirical evidence using a simultaneous model. Transport Policy, 35, 350-357. https://doi.org/10.1016/j.tranpol.2014.04.008
Gao, X., & Zhao, Y. (2025). Research on methods for the recognition of ship lights and the autonomous determination of the types of approaching vessels. Journal of Marine Science and Engineering, 13(4), 643. https://doi.org/10.3390/jmse13040643
Hsu, Y.-C. (2023). Assessment of criteria of ship classification societies. Maritime Policy & Management, 50(7), 980-1004. https://doi.org/10.1080/03088839.2022.2061061
IMO. (2005). MSC/Circular.1144 – Additional Guidance for the Uniform Application of Rule 1(e) of the International Regulations for Preventing Collisions at Sea, 1972, as Amended – (13 December 2004). International Maritime Organization.
IMO. (2007). Resolution MSC 253(86) Adoption of the Performance Standards for Navigation Lights, Navigation Light Controllers and Associated Equipment. International Maritime Organization.
IMO. (2009). SN.1/Circ.293: Exemption Granted under Rule 1(e) of the International Regulations for Preventing Collisions at Sea, 1972. International Maritime Organization.
IMO. (2013). Resolution A.1085(28) Amendments to the Convention on the International Regulations for Preventing Collisions at Sea, 1972. International Maritime Organization.
IMO. (2014). Unified Interpretations of Colreg 1972, As Amended Msc.1/Circ. 1260/Rev1. International Maritime Organization.
IMO. (2017). Unified Interpretation on the Application of Colreg with Respect to the Placement of Sidelights - Msc.1/Circ. 1577. International Maritime Organization.
IMO. (2018). Convention on the International Regulations for Preventing Collisions at Sea, 1972 (Consolidated edition). International Maritime Organization.
IMO. (2023). Resolution A.1185(33) Procedures for Port State Control, 2023. International Maritime Organization.
IMO. (2025). COLREG - Preventing collisions at sea. International Maritime Organization.
Jo, M., Choi, W., Lim, M., Seo, S., & Shin, J. (2024). A study on improving the international regulations for preventing collisions at sea (COLREG) for the introduction of maritime autonomous surface ships (MASS). Journal of International Maritime Safety, Environmental Affairs, and Shipping, 8(4), 2428006. https://doi.org/10.1080/25725084.2024.2428006
Kara, E. G. E. (2022). Determination of maritime safety performance of flag states based on the Port State Control inspections using TOPSIS. Marine Policy, 143, 105156. https://doi.org/10.1016/j.marpol.2022.105156
Khalique, A., Bury, A., & Loughney, S. (2024). Analysis and optimisation of best practice for proper lookout at night. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 18(2), 357–367. https://doi.org/10.12716/1001.18.02.12
Kim, J.-K., & Park, D.-J. (2024). Understanding of sailing rule based on COLREGs: Comparison of navigator survey and automated collision-avoidance algorithm. Marine Policy, 159, 105894. https://doi.org/10.1016/j.marpol.2023.105894
Knapp, S. (2021). Quantification and analysis of risk exposure in the maritime industry: Averted incident costs due to inspections and the effect of SARS-Cov-2 (COVID-19). Safety, 7(2), 43. https://doi.org/10.3390/safety7020043
Kundakçı, B., & Sevgili, C. (2025). Identifying hidden patterns in offshore vessel accidents using association rule mining. Ships and Offshore Structures, 1-15. https://doi.org/10.1080/17445302.2025.2547812
Li, K. X., & Zheng, H. (2008). Enforcement of law by the Port State Control (PSC). Maritime Policy & Management, 35(1), 61-71. https://doi.org/10.1080/03088830701848912
Margat, P., & Stadermann, M. G. (2024). The application of COLREGs by autonomous and unmanned vessels: Issues raised by situational awareness, night-time navigation and good seamanship. 4th European Workshop on Maritime Systems, Resilience and Security 2024 (MARESEC 24), Deutschland. pp. 12.
Martelli, M., Žuškin, S., Zaccone, R., & Rudan, I. (2023). A COLREGs-compliant decision support tool to prevent collisions at sea. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 17(2), 347-353. https://doi.org/10.12716/1001.17.02.11
Maza, J. A. G., & Argüelles, R. P. (2022). COLREGs and their application in collision avoidance algorithms: A critical analysis. Ocean Engineering, 261, 112029. https://doi.org/10.1016/j.oceaneng.2022.112029
Mitroussi, K., & Arghyrou, M. G. (2016). Institutional performance and ship registration. Transportation Research Part E: Logistics and Transportation Review, 85, 90–106. https://doi.org/10.1016/j.tre.2015.10.004
Norwegian Maritime Authority. (2023). Acceptance criteria for granting certain exemptions and equivalencies to ships registered in a Norwegian ship register (IC 2 - 2017). Case number: 2023/15495. Norwegian Maritime Authority.
Özçayır, O. (2018). Port state control. Informa Law from Routledge.
Öztürk, Ü. (2025). Acceptance criteria for collision avoidance of autonomous navigation systems in approval process. Journal of Physics: Conference Series, 3123(1), 012017. https://doi.org/10.1088/1742-6596/3123/1/012017
Pantenia. (2012). Study on Pilotage Exemption Certificates. Final report. PwC and Panteia.
Sánchez, R. J., Perrotti, D. E., & Fort, A. G. P. (2021). Looking into the future ten years later: Big full containerships and their arrival to south American ports. Journal of Shipping and Trade, 6(1), 2. https://doi.org/10.1186/s41072-021-00083-5
Sevgili, C. (2024). Evaluation of pollution prevention related deficiencies of ships using association rule mining. Marine Pollution Bulletin, 208, 116938. https://doi.org/10.1016/j.marpolbul.2024.116938
Stępień, B. (2024). Towards a new horizon: 1972 COLREG in the era of autonomous ships. Ocean Development & International Law, 55(1-2), 170-184. https://doi.org/10.1080/00908320.2024.2359908
Türk Loydu. (2013). Unified Interpretations for the Implementation of COLREG – Convention on the International Regulations for Preventing Collisions at Sea. Türk Loydu.
Türk Loydu. (2019). Interpretation to COLREG 1972 Annex 1, Section 3 (b). Türk Loydu.
Van Lynden, C., Van Winsen, I., Westland, C., & Kana, A. (2022). Offshore wind installation vessels: Generating insight about the driving factors behind the future design. International Journal of Maritime Engineering, 164(A2), 221–236. https://doi.org/10.5750/ijme.v164iA2.1175
Wang, C., Fan, B., Li, Y., Xiao, J., Min, L., Zhang, J., Chen, J., Lin, Z., Su, S., & Wu, R. (2023). Study on the classification perception and visibility enhancement of ship navigation environments in foggy conditions. Journal of Marine Science and Engineering, 11(7), 1298. https://doi.org/10.3390/jmse11071298
Wang, Z., & Yin, J. (2020). Risk assessment of inland waterborne transportation using data mining. Maritime Policy & Management, 47(5), 633-648. https://doi.org/10.1080/03088839.2020.1738582
Wei, H. (2019). Quantitative analysis of fleet operation status based on fleet development status model OSV (Offshore Support Vessel) as a ship for energy exploitation and use. 2019 Proceedings of the 2019 2nd International Conference on Sustainable Energy, Environment and Information Engineering (pp. 42-44). https://doi.org/10.2991/seeie-19.2019.10
Wróbel, K., Gil, M., Huang, Y., & Wawruch, R. (2022). The vagueness of COLREG versus collision avoidance techniques—A discussion on the current state and future challenges concerning the operation of autonomous ships. Sustainability, 14(24), 16516. https://doi.org/10.3390/su142416516
Wynn, T., Howarth, P. A., & Kunze, B. R. (2012). Night-time lookout duty: The role of ambient light levels and dark adaptation. The Journal of Navigation, 65(4), 589-602. https://doi.org/10.1017/S0373463312000288
Xu, T., Guan, J., & Xia, W. (2020). Review on “a vessel of special construction or purpose” in relation to collision prevention. Journal of International Maritime Safety, Environmental Affairs, and Shipping, 4(3), 76–79. https://doi.org/10.1080/25725084.2020.1790109
Yan, R., & Wang, S. (2019). Ship inspection by port state control—review of current research. In:X. Qu, L. Zhen, R. Howlett, & L. Jain (Eds.), Smart Transportation Systems 2019. Smart Innovation, Systems and Technologies, vol 149 (pp. 233-241). Springer, https://doi.org/10.1007/978-981-13-8683-1_24
Yu, W., Liu, J., Yan, P., & Jiang, X. (2025). Research on the impact of deep sea offshore wind farms on maritime safety. Journal of Marine Science and Engineering, 13(4), 699. https://doi.org/10.3390/jmse13040699
Yu, Y., Chen, L., Shu, Y., & Zhu, W. (2021). Evaluation model and management strategy for reducing pollution caused by ship collision in coastal waters. Ocean & Coastal Management, 203, 105446. https://doi.org/10.1016/j.ocecoaman.2020.105446
Zaccone, R. (2021). COLREG-compliant optimal path planning for real-time guidance and control of autonomous ships. Journal of Marine Science and Engineering, 9(4), 405. https://doi.org/10.3390/jmse9040405

Details

Primary Language

English

Subjects

Maritime Business Administration , Marine Transportation , Maritime Transportation Engineering

Journal Section

Research Article

Authors

Taha Talip Türkistanlı ^*
0000-0003-4903-6138
Türkiye

Early Pub Date

March 11, 2026

Publication Date

March 11, 2026

Submission Date

January 3, 2026

Acceptance Date

February 11, 2026

Published in Issue

Year 1970 Volume: 15 Number: 1

DOI

https://doi.org/10.33714/masteb.1855338

IZ

https://izlik.org/JA75EK73CF

Cite

RIS / Bibtex

APA

Türkistanlı, T. T. (2026). Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis. Marine Science and Technology Bulletin, 15(1), 9-27. https://doi.org/10.33714/masteb.1855338

AMA

1.Türkistanlı TT. Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis. Mar. Sci. Tech. Bull. 2026;15(1):9-27. doi:10.33714/masteb.1855338

Chicago

Türkistanlı, Taha Talip. 2026. “Operationalizing COLREG Exemptions and Equivalents: Data-Driven Evidence via Association Rule Mining and Network Analysis”. Marine Science and Technology Bulletin 15 (1): 9-27. https://doi.org/10.33714/masteb.1855338.

EndNote

Türkistanlı TT (March 1, 2026) Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis. Marine Science and Technology Bulletin 15 1 9–27.

IEEE

[1]T. T. Türkistanlı, “Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis”, Mar. Sci. Tech. Bull., vol. 15, no. 1, pp. 9–27, Mar. 2026, doi: 10.33714/masteb.1855338.

ISNAD

Türkistanlı, Taha Talip. “Operationalizing COLREG Exemptions and Equivalents: Data-Driven Evidence via Association Rule Mining and Network Analysis”. Marine Science and Technology Bulletin 15/1 (March 1, 2026): 9-27. https://doi.org/10.33714/masteb.1855338.

JAMA

1.Türkistanlı TT. Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis. Mar. Sci. Tech. Bull. 2026;15:9–27.

MLA

Türkistanlı, Taha Talip. “Operationalizing COLREG Exemptions and Equivalents: Data-Driven Evidence via Association Rule Mining and Network Analysis”. Marine Science and Technology Bulletin, vol. 15, no. 1, Mar. 2026, pp. 9-27, doi:10.33714/masteb.1855338.

Vancouver

1.Taha Talip Türkistanlı. Operationalizing COLREG exemptions and equivalents: Data-driven evidence via association rule mining and network analysis. Mar. Sci. Tech. Bull. 2026 Mar. 1;15(1):9-27. doi:10.33714/masteb.1855338