Reliance Ahead of Understanding: Practitioner Assessment of AIS Effectiveness in Vessel Traffic Service Operations at Merak, Sunda Strait
DOI:
https://doi.org/10.55927/ijsmr.v4i9.154Keywords:
Automatic Identification System, Vessel Traffic Service, Collision prevention, Sunda Strait, Maritime situational awarenessAbstract
This study examines practitioner assessment of AIS in supporting Vessel Traffic Service operations and collision prevention at Merak, on the Sunda Strait. A descriptive quantitative survey using a five-point Likert instrument was administered to 60 respondents comprising VTS Merak operators, ship crew, and navigation officers, supplemented by limited observation and interview. Four aspects were assessed. Effectiveness for safety attained the highest mean at 4.32, followed by the perceived need for further development at 4.28, implementation within VTS operations at 4.18, and understanding of AIS functions at 4.06. The profile discloses a pattern the study terms reliance ahead of understanding: the aspect concerning operational benefit scored highest while the aspect concerning knowledge of the system scored lowest, a gap of 0.26 scale points. Respondents simultaneously endorsed AIS and identified persistent constraints, principally signal interference and demand for technical training, alongside support for Satellite AIS integration.
References
Ahmed, Y. A., Hannan, M. A., Oraby, M. Y., & Maimun, A. (2021). COLREGs compliant fuzzy-based collision avoidance system for multiple ship encounters. Journal of Marine Science and Engineering, 9(8), Article 790. https://doi.org/10.3390/jmse9080790
Androjna, A., Brcko, T., Pavic, I., & Greidanus, H. (2020). Assessing cyber challenges of maritime navigation. Journal of Marine Science and Engineering, 8(10), Article 776. https://doi.org/10.3390/jmse8100776
Androjna, A., Perkovic, M., Pavic, I., & Miskovic, J. (2021). AIS data vulnerability indicated by a spoofing case-study. Applied Sciences, 11(11), Article 5015. https://doi.org/10.3390/app11115015
Bacnar, D., Baric, D., & Ogrizovic, D. (2025). Charting the future of maritime education and training: A technology-acceptance-model-based pilot study on students' behavioural intention to use a fully immersive VR engine room simulator. Applied System Innovation, 8(3), Article 84. https://doi.org/10.3390/asi8030084
Baldauf, M., Benedict, K., & Kruger, C. (2014). Potentials of e-Navigation: Enhanced support for collision avoidance. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 8(4), 613-617. https://doi.org/10.12716/1001.08.04.18
Baldauf, M., Claresta, G., & Nugroho, T. F. (2020). Vessel Traffic Services (VTS) to ensure safety of maritime transportation: Studies of potentials in Sunda Strait. IOP Conference Series: Earth and Environmental Science, 557(1), Article 012068. https://doi.org/10.1088/1755-1315/557/1/012068
Car, M., Brcic, D., Zuskin, S., & Svilicic, B. (2020). The navigator's aspect of PNC before and after ECDIS implementation: Facts and potential implications towards navigation safety improvement. Journal of Marine Science and Engineering, 8(11), Article 842. https://doi.org/10.3390/jmse8110842
Cervera, M. A., Ginesi, A., & Eckstein, K. (2011). Satellite-based vessel Automatic Identification System: A feasibility and performance analysis. International Journal of Satellite Communications and Networking, 29(2), 117-142. https://doi.org/10.1002/sat.957
Chen, P., Huang, Y., Mou, J., & van Gelder, P. H. A. J. M. (2019). Probabilistic risk analysis for ship-ship collision: State-of-the-art. Safety Science, 117, 108-122. https://doi.org/10.1016/j.ssci.2019.04.014
Crestelo Moreno, F., Roca Gonzalez, J., Suardiaz Muro, J., & Garcia Maza, J. A. (2022). Relationship between human factors and a safe performance of vessel traffic service operators: A systematic qualitative-based review in maritime safety. Safety Science, 155, Article 105892. https://doi.org/10.1016/j.ssci.2022.105892
Crestelo Moreno, F., Soto-Lopez, V., Menendez-Telena, D., Roca-Gonzalez, J., Suardiaz Muro, J., Roces, C., Paino, M., Fernandez, I., & Diaz-Secades, L. A. (2023). Fatigue as a key human factor in complex sociotechnical systems: Vessel Traffic Services. Frontiers in Public Health, 11, Article 1160971. https://doi.org/10.3389/fpubh.2023.1160971
Eriksen, T., Hoye, G., Narheim, B., & Meland, B. J. (2006). Maritime traffic monitoring using a space-based AIS receiver. Acta Astronautica, 58(10), 537-549. https://doi.org/10.1016/j.actaastro.2005.12.016
Fournier, M., Casey Hilliard, R., Rezaee, S., & Pelot, R. (2018). Past, present, and future of the satellite-based automatic identification system: Areas of applications (2004-2016). WMU Journal of Maritime Affairs, 17(3), 311-345. https://doi.org/10.1007/s13437-018-0151-6
Garcia Maza, J. A., & Arguelles, R. P. (2022). COLREGs and their application in collision avoidance algorithms: A critical analysis. Ocean Engineering, 261, Article 112029. https://doi.org/10.1016/j.oceaneng.2022.112029
Goudossis, A., & Katsikas, S. K. (2018). Towards a secure automatic identification system (AIS). Journal of Marine Science and Technology, 24(2), 410-423. https://doi.org/10.1007/s00773-018-0561-3
Harati-Mokhtari, A., Wall, A., Brooks, P., & Wang, J. (2007). Automatic Identification System (AIS): Data reliability and human error implications. Journal of Navigation, 60(3), 373-389. https://doi.org/10.1017/S0373463307004298
Kim, K., Lee, C., & Lim, D. (2025). Understanding seafarers' acceptance of the transition to alternative fuels in shipping through the technology acceptance model. Journal of Marine Science and Engineering, 13(12), Article 2308. https://doi.org/10.3390/jmse13122308
Kim, T., Sharma, A., Bustgaard, M., Gyldensten, W. C., Nymoen, O. K., Tusher, H. M., & Nazir, S. (2021). The continuum of simulator-based maritime training and education. WMU Journal of Maritime Affairs, 20(2), 135-150. https://doi.org/10.1007/s13437-021-00242-2
Li, F., Chen, C.-H., Xu, G., Chang, D., & Khoo, L. P. (2020). Causal factors and symptoms of task-related human fatigue in vessel traffic service: A task-driven approach. Journal of Navigation, 73(6), 1340-1357. https://doi.org/10.1017/S0373463320000326
Nguyen, D., Vadaine, R., Hajduch, G., Garello, R., & Fablet, R. (2022). GeoTrackNet: A maritime anomaly detector using probabilistic neural network representation of AIS tracks and a contrario detection. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5655-5667. https://doi.org/10.1109/TITS.2021.3055614
Nofandi, F., Widyaningsih, U., Rakhman, R. A., Mirianto, A., Zuhri, Z., & Harini, N. V. (2022). Case study of ship traffic crowds in the Malacca Strait-Singapore by using vessel traffic system. IOP Conference Series: Earth and Environmental Science, 1081(1), Article 012009. https://doi.org/10.1088/1755-1315/1081/1/012009
Praetorius, G., Hollnagel, E., & Dahlman, J. (2015). Modelling Vessel Traffic Service to understand resilience in everyday operations. Reliability Engineering & System Safety, 141, 10-21. https://doi.org/10.1016/j.ress.2015.03.020
Priadi, A. A., Ivan, R., Darsani, & Anindhyta, C. (2024). Evaluation of the implementation of traffic separation scheme (TSS) in the Sunda Strait. IOP Conference Series: Earth and Environmental Science, 1294(1), Article 012028. https://doi.org/10.1088/1755-1315/1294/1/012028
Qu, X., Meng, Q., & Suyi, L. (2011). Ship collision risk assessment for the Singapore Strait. Accident Analysis & Prevention, 43(6), 2030-2036. https://doi.org/10.1016/j.aap.2011.05.022
Relling, T., Lutzhoft, M., Ostnes, R., & Hildre, H. P. (2021). The contribution of Vessel Traffic Services to safe coexistence between automated and conventional vessels. Maritime Policy & Management, 49(7), 990-1009. https://doi.org/10.1080/03088839.2021.1937739
Rong, H., Teixeira, A. P., & Guedes Soares, C. (2020). Data mining approach to shipping route characterization and anomaly detection based on AIS data. Ocean Engineering, 198, Article 106936. https://doi.org/10.1016/j.oceaneng.2020.106936
Sharma, A., Mallam, S., MacKinnon, S. N., & Saetrevik, B. (2025). A systematic review of cognitive and social factors in vessel traffic services operations. Transport Reviews, 46(2), 322-343. https://doi.org/10.1080/01441647.2025.2569578
Sharma, A., Nazir, S., & Ernstsen, J. (2019). Situation awareness information requirements for maritime navigation: A goal directed task analysis. Safety Science, 120, 745-752. https://doi.org/10.1016/j.ssci.2019.08.016
Sobaruddin, D. P., Armawi, A., & Giyarsih, S. R. (2026). The development of inshore traffic zone in Sunda Strait. Indonesian Journal of Geography, 58(1), 102-117. https://doi.org/10.22146/ijg.115071
Soldi, G., Gaglione, D., Forti, N., De Simone, A., Daffina, F. C., Bottini, G., Quattrociocchi, D., Millefiori, L. M., Braca, P., Carniel, S., Willett, P., Iodice, A., Riccio, D., & Farina, A. (2021). Space-based global maritime surveillance. Part I: Satellite technologies. IEEE Aerospace and Electronic Systems Magazine, 36(9), 8-28. https://doi.org/10.1109/MAES.2021.3070862
Sunaryo, S., Priadi, A. A., & Tjahjono, T. (2015). Implementation of traffic separation scheme for preventing accidents on the Sunda Strait. International Journal of Technology, 6(6), 990-997. https://doi.org/10.14716/ijtech.v6i6.1966
Svanberg, M., Santen, V., Horteborn, A., Holm, H., & Finnsgard, C. (2019). AIS in maritime research. Marine Policy, 106, Article 103520. https://doi.org/10.1016/j.marpol.2019.103520
Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., & Huang, G.-B. (2018). Exploiting AIS data for intelligent maritime navigation: A comprehensive survey from data to methodology. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1559-1582. https://doi.org/10.1109/TITS.2017.2724551
van de Merwe, K., Mallam, S., & Nazir, S. (2022). Agent transparency, situation awareness, mental workload, and operator performance: A systematic literature review. Human Factors, 66(1), 180-208. https://doi.org/10.1177/00187208221077804
Weintrit, A. (2013). Prioritized main potential solutions for the e-Navigation concept. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 7(2), 27-38. https://doi.org/10.12716/1001.07.01.03
Wolsing, K., Roepert, L., Bauer, J., & Wehrle, K. (2022). Anomaly detection in maritime AIS tracks: A review of recent approaches. Journal of Marine Science and Engineering, 10(1), Article 112. https://doi.org/10.3390/jmse10010112
Yan, Z., Xiao, Y., Cheng, L., He, R., Ruan, X., Zhou, X., Li, M., & Bin, R. (2020). Exploring AIS data for intelligent maritime routes extraction. Applied Ocean Research, 101, Article 102271. https://doi.org/10.1016/j.apor.2020.102271
Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. X. (2019). How big data enriches maritime research: A critical review of Automatic Identification System (AIS) data applications. Transport Reviews, 39(6), 755-773. https://doi.org/10.1080/01441647.2019.1649315
Zaman, M. B., Kobayashi, E., & Zubaydi, A. (2021). Traffic analysis for enhancing safety in the Singapore Straits using AIS data. IOP Conference Series: Earth and Environmental Science, 649(1), Article 012065. https://doi.org/10.1088/1755-1315/649/1/012065
Zhang, L., & Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, Article 106130. https://doi.org/10.1016/j.oceaneng.2019.106130
Zhang, W., Goerlandt, F., Kujala, P., & Wang, Y. (2016). An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124, 141-156. https://doi.org/10.1016/j.oceaneng.2016.07.059
Zhao, L., & Shi, G. (2019). Maritime anomaly detection using density-based clustering and recurrent neural network. Journal of Navigation, 72(4), 894-916. https://doi.org/10.1017/S0373463319000031
Zhen, R., Riveiro, M., & Jin, Y. (2017). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, 492-501. https://doi.org/10.1016/j.oceaneng.2017.09.015
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