DOI QR코드

DOI QR Code

A Study on Improvement of Maritime Traffic Analysis Using Shape Format Data for Maritime Autonomous Surface Ships

자율운항선박 도입을 위한 수치해도 데이터 활용 해상교통분석 개선방안

  • Hwang, Taewoong (Department of Maritime Transportation System Mokpo National Maritime University) ;
  • Hwang, Taemin (Department of Maritime Transportation System Mokpo National Maritime University) ;
  • Youn, Ik-Hyun (Division of Navigation & Information Systems, Mokpo National Maritime University)
  • 황태웅 (목포해양대학교 해상운송시스템학부) ;
  • 황태민 (목포해양대학교 해상운송시스템학부) ;
  • 윤익현 (목포해양대학교 항해정보시스템학부)
  • Received : 2022.09.30
  • Accepted : 2022.10.28
  • Published : 2022.10.31

Abstract

The maritime traffic analysis has been conducted in various ways to solve problems arising from the complex marine environment. However, recent trends in the maritime industry, such as the development of the maritime autonomous surface ships (MASS), suggest that maritime traf ic analysis needs change. Accordingly, based on the studies conducted over the past decade for improvements, automatic identification system (AIS) data is mainly used for maritime traffic analysis. Moreover, the use of geographic information that directly af ects ship operation is relatively insufficient. Therefore, this study presented a method of using a combination of shape format data and AIS data to enhance maritime traffic analysis in preparation for the commercialization of autonomous ships. Consequently, extractable marine traffic characteristics were presented when shape format data were used for marine traffic analysis. This is expected to be used for marine traffic analysis for the introduction of autonomous ships in the future.

해상교통분석은 복잡해지는 해양환경에 따라 발생하는 문제해결을 위해 다방면으로 시행되고 있다. 하지만 4차 산업혁명으로부터 도래된 자율운항선박 개발 등의 해사분야 동향은 해상교통분석에도 변화가 필요함을 암시한다. 이에 해상교통분석의 개선점을 식별하고자 관련 연구를 분석하였으며, AIS데이터의 활용도가 높은 반면에 해도정보의 활용은 그 중요도에 비해 부족한 것으로 조사되었다. 이에 본 연구는 자율운항선박의 상용화에 대비한 해상교통분석의 개선점으로서 수치해도 데이터와 선박운항데이터인 AIS데이터를 복합적으로 활용하는 방법을 제시하였다. 연구결과로써 해상교통분석에 수치해도데이터를 활용하였을 때 추출 가능한 해상교통특성을 제시하였으며 이는 향후 자율운항선박의 도입을 위한 해상교통분석에 활용가능할 것으로 기대된다.

Keywords

Acknowledgement

본 논문은 2022년도 해양수산부 및 해양수산과학기술진흥원 연구비 지원으로 수행된 '자율운항선박 기술개발사업(20200615)'의 연구결과입니다.

References

  1. Abreu, F. H. O., A. Soares, F. V. Paulovich, and S. Matwin(2021b), A trajectory scoring tool for local anomaly detection in maritime traffic using visual analytics. ISPRS International Journal of Geo-Information, 10(6), 412. https://doi.org/10.3390/ijgi10060412
  2. Abreu, F. H. O., A. Soares, F. V. Paulovich, and S. Matwin(2021a), Local anomaly detection in maritime traffic using visual analytics. Paper presented at the EDBT/ICDT Workshops.
  3. Acomi, N.(2020), Impact of chart data accuracy on the safety of navigation. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 14(2).
  4. Altan, Y. C. and E. N. Otay(2017), Maritime traffic analysis of the strait of istanbul based on AIS data. The Journal of Navigation, 70(6), pp. 1367-1382. https://doi.org/10.1017/S0373463317000431
  5. Alvarez, N. G., B. Adenso-Diaz, and L. Calzada-Infant(2021), Maritime traffic as a complex network: A systematic review. Networks and Spatial Economics, 21(2), pp. 387-417. https://doi.org/10.1007/s11067-021-09528-7
  6. Aps, R., M. Fetissov, F. Goerlandt, P. Kujala, and A. Piel(2017), Systems-theoretic process analysis of maritime traffic safety management in the gulf of finland (baltic sea), Procedia Engineering, 179, pp. 2-12. https://doi.org/10.1016/j.proeng.2017.03.090
  7. Arguedas, V. F., G. Pallotta, and M. Vespe(2014), Automatic generation of geographical networks for maritime traffic surveillance. Paper presented at the 17th International Conference on Information Fusion (FUSION), pp. 1-8.
  8. Arguedas, V. F., G. Pallotta, and M. Vespe(2017), Maritime traffic networks: From historical positioning data to unsupervised maritime traffic monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(3), pp. 722-732. https://doi.org/10.1109/TITS.2017.2699635
  9. Baldauf, M., K. Benedict, S. Fischer, F. Motz, and J. U. Schroder-Hinrichs(2011), Collision avoidance systems in air and maritime traffic. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 225(3), pp. 333-343. https://doi.org/10.1177/1748006X11408973
  10. Bodunov, O., F. Schmidt, A. Martin, A. Brito, and C. Fetzer(2018), Real-time destination and eta prediction for maritime traffic. Paper presented at the Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems, pp. 198-201.
  11. Campana, I., D. Angeletti, R. Crosti, C. Luperini, A. Ruvolo, A. Alessandrini, et al.(2017), Seasonal characterisation of maritime traffic and the relationship with cetacean presence in the western mediterranean sea. Marine Pollution Bulletin, 115(1-2), pp. 282-291. https://doi.org/10.1016/j.marpolbul.2016.12.008
  12. Campana, I., R. Crosti, D. Angeletti, L. Carosso, L. David, N. Di-Meglio, et al.(2015), Cetacean response to summer maritime traffic in the western mediterranean sea. Marine Environmental Research, 109, pp. 1-8. https://doi.org/10.1016/j.marenvres.2015.05.009
  13. Coscia, P., P. Braca, L. M. Millefiori, F. A. Palmieri, and P. Willett(2018), Multiple Ornstein-Uhlenbeck processes for maritime traffic graph representation. IEEE Transactions on Aerospace and Electronic Systems, 54(5), pp. 2158-2170. https://doi.org/10.1109/TAES.2018.2808098
  14. Du, L., F. Goerlandt, and P. Kujala(2020), Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data. Reliability Engineering and System Safety, 200, 106933. https://doi.org/10.1016/j.ress.2020.106933
  15. Feng, X. X., M. Zhang, and Z. Liu(2012), Analysis on sediment environment and waterway siltation characteristics of panjin port. Paper presented at the Applied Mechanics and Materials, 212. pp. 205-210. https://doi.org/10.4028/www.scientific.net/AMM.212-213.205
  16. Gaspar, J. A., and H. Leitao(2018), What is a nautical chart, really? uncovering the geometry of early modern nautical charts. Journal of Cultural Heritage, 29, pp. 130-136. https://doi.org/10.1016/j.culher.2017.09.008
  17. Hanninen, M.(2014), Bayesian networks for maritime traffic accident prevention: Benefits and challenges. Accident Analysis and Prevention, 73, pp. 305-312. https://doi.org/10.1016/j.aap.2014.09.017
  18. Hofbauer, F. and L. Putz(2020), External costs in inland waterway transport: An analysis of external cost categories and calculation methods. Sustainability, 12(14), 5874. https://doi.org/10.3390/su12145874
  19. Hwang, T. and I. Youn(2021). Navigation Situation Clustering Model of Human-Operated Ships for Maritime Autonomous Surface Ship Collision Avoidance Tests. Journal of Marine Science and Engineering, 9(12), 1458. https://doi.org/10.3390/jmse9121458
  20. Jeong, J. S., G. Park, and K. I. Kim(2012), Risk assessment model of maritime traffic in time-variant CPA environments in waterway. Journal of Advanced Computational Intelligence and Intelligent Informatics, 16(7), pp. 866-873. https://doi.org/10.20965/jaciii.2012.p0866
  21. Jiacai, P., J. Qingshan, H. Jinxing, and S. Zheping(2012), An AIS data visualization model for assessing maritime traffic situation and its applications. Procedia Engineering, 29, pp. 365-369. https://doi.org/10.1016/j.proeng.2011.12.724
  22. Kim, D., H. Shin, and D. Jang(2020), Analysis of Long-Term Variation in Marine Traffic Volume and Characteristics of Ship Traffic Routes in Yeosu Gwangyang Port. The Korean Society of Marine Environment and Safety, 26(1), pp. 31-38. https://doi.org/10.7837/kosomes.2020.26.1.031
  23. Kim, D., J. Park, and Y. Park(2011a), Comparison analysis between the IWRAP and the ES model in ulsan waterway. Journal of Navigation and Port Research, 35(4), pp. 281-287. https://doi.org/10.5394/KINPR.2011.35.4.281
  24. Kim, K., G. Park, and J. Jeong(2011b), Analysis of marine accident probability in mokpo waterways. Journal of Navigation and Port Research, 35(9), pp. 729-733. https://doi.org/10.5394/KINPR.2011.35.9.729
  25. Kim, K., J. S. Jeong, and G. Park(2012), A study on development of maritime traffic assessment model. Journal of the Korean Institute of Intelligent Systems, 22(6), pp. 761-767. https://doi.org/10.5391/JKIIS.2012.22.6.761
  26. Kim, K., J. S. Jeong, and G. Park(2013), Assessment of external force acting on ship using big data in maritime traffic. Journal of the Korean Institute of Intelligent Systems, 23(5), pp. 379-384. https://doi.org/10.5391/JKIIS.2013.23.5.379
  27. Kim, S., H. Rhee, and I. Gong(2017), Improving assessments of maritime traffic congestion based on occupancy area density analysis for traffic vessels. Journal of the Korean Society of Marine Environment and Safety, 23(2), pp. 153-160. https://doi.org/10.7837/kosomes.2017.23.2.153
  28. Kontopoulos, I., I. Varlamis, and K. Tserpes(2021), A distributed framework for extracting maritime traffic patterns. International Journal of Geographical Information Science, 35(4), pp. 767-792. https://doi.org/10.1080/13658816.2020.1792914
  29. Lei, P.(2020), Mining maritime traffic conflict trajectories from a massive AIS data. Knowledge and Information Systems, 62(1), pp. 259-285. https://doi.org/10.1007/s10115-019-01355-0
  30. Lei, P., T. Tsai, and W. Peng(2016), Discovering maritime traffic route from AIS network. Paper presented at the 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1-6.
  31. Li, Q., B. Zhan, and Q. B. Zhang(2013), The analysis of feasibility between waterway transportation and economy of hubei province based on DEA model. Paper presented at the Advanced Materials Research, , 694. pp. 3333-3335.
  32. Lu, N., M. Liang, R. Zheng, and R. W. Liu(2020), Historical AIS data-driven unsupervised automatic extraction of directional maritime traffic networks. Paper presented at the 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), pp. 7-12.
  33. Mazaheri, A., J. Montewka, P. Kotilainen, O. E. Sormunen, and P. Kujala(2015), Assessing grounding frequency using ship traffic and waterway complexity. The Journal of Navigation, 68(1), pp. 89-106. https://doi.org/10.1017/S0373463314000502
  34. Mansson, J. T., M. Lutzhoft, and B. Brooks(2017), Joint activity in the maritime traffic system: Perceptions of ship masters, maritime pilots, tug masters, and vessel traffic service operators. The Journal of Navigation, 70(3), pp. 547-560. https://doi.org/10.1017/S0373463316000758
  35. Mehta, V. A. Zaloom, and B. N. Craig(2016), Analysis of waterway transportation in southeast texas waterway based on AIS data. Ocean Engineering, 121, pp. 196-209. https://doi.org/10.1016/j.oceaneng.2016.05.012
  36. Mladineo, N., M. Mladineo, and S. Knezic(2017), Web MCA-based decision support system for incident situations in maritime traffic: Case study of adriatic sea. The Journal of Navigation, 70(6), pp. 1312-1334. https://doi.org/10.1017/S0373463317000388
  37. Oh, J. and H. Kim(2020), Spatiotemporal Analysis of Vessel Trajectory Data using Network Analysis. Journal of the Korean Society of Marine Environment and Safety, 26(7), pp 759-766. https://doi.org/10.7837/kosomes.2020.26.7.759
  38. Probha, N. A. and M. S. Hoque(2018), A study on transport safety perspectives in bangladesh through comparative analysis of roadway, railway and waterway accidents. Paper presented at the Proceedings of the Asia-Pacific Conference on Intelligent Medical 2018 and International Conference on Transportation and Traffic Engineering 2018, pp. 81-85.
  39. Perera, L. P., P. Oliveira, and C. G. Soares(2012), Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Transactions on Intelligent Transportation Systems, 13(3), pp. 1188-1200. https://doi.org/10.1109/TITS.2012.2187282
  40. Praetorius, G. and E. Hollnagel(2014), Control and resilience within the maritime traffic management domain. Journal of Cognitive Engineering and Decision Making, 8(4), pp. 303-317. https://doi.org/10.1177/1555343414560022
  41. Praetorius, G.(2014), Vessel Traffic Service (VTS): A Maritime Information Service Or Traffic Control System?: Understanding Everyday Performance and Resilience in a Socio-Technical System Under Change.
  42. Ray, C., A. Grancher, R. Thibaud, and L. Etienne(2013), Spatio-temporal rule-based analysis of maritime traffic. Paper presented at the Third Conference on Ocean and Coastal Observation: Sensors and Observing Systems, Numerical Models and Information (OCOSS).
  43. Reed, S. and V. E. Schmidt(2016), Providing nautical chart awareness to autonomous surface vessel operations. Paper presented at the OCEANS 2016 MTS/IEEE Monterey, pp. 1-8.
  44. Robards, M. D., G. K. Silber, J. D. Adams, J. Arroyo, D. Lorenzini, K. Schwehr, et al.(2016), Conservation science and policy applications of the marine vessel automatic identification system (AIS) - a review. Bulletin of Marine Science, 92(1), pp. 75-103. https://doi.org/10.5343/bms.2015.1034
  45. Sang, L., A. Wall, Z. Mao, X. Yan, J. and Wang(2015), A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Engineering, 110, pp. 183-194. https://doi.org/10.1016/j.oceaneng.2015.10.021
  46. Serry, A.(2016), The automatic identification system (AIS): A data source for studying maritime traffic. Paper presented at the Maritime Transport'16.
  47. Tafa, L. N., X. Su, J. Hong, and C. Choi(2019), Automatic maritime traffic synthetic route: A framework for route prediction. Paper presented at the International Symposium on Pervasive Systems, Algorithms and Networks, pp. 3-14.
  48. Teixeira, A. P. and C. Guedes Soares(2018), Risk of maritime traffic in coastal waters. Paper presented at the International Conference on Offshore Mechanics and Arctic Engineering, 51326, pp. V11AT12A025.
  49. Vanek, O., M. Jakob, O. Hrstka, and M. Pechoucek(2013), Agent-based model of maritime traffic in piracy-affected waters. Transportation Research Part C: Emerging Technologies, 36, pp. 157-176. https://doi.org/10.1016/j.trc.2013.08.009
  50. Vespe, M., I. Visentini, K. Bryan, and P. Braca(2012), Unsupervised learning of maritime traffic patterns for anomaly detection.
  51. Venskus, J., P. Treigys, J. Bernataviciene, G. Tamulevicius, and V. Medvedev(2019), Real-time maritime traffic anomaly detection based on sensors and history data embedding. Sensors, 19(17), 3782. https://doi.org/10.3390/s19173782
  52. Wei, L., Y. Xiaowen, and L. Chunxia(2013), Analysis of container transportation in yangtze river delta: Waterway-road transport versus road transport. Journal of Chongqing Jiaotong University (Natural Science), 32(2), p. 274.
  53. Xiao, F., H. Ligteringen, C. Van Gulijk, and B. Ale(2012), Artificial force fields for multi-agent simulations of maritime traffic: A case study of chinese waterway. Procedia Engineering, 45, pp. 807-814. https://doi.org/10.1016/j.proeng.2012.08.243
  54. Xiao, Z., X. Fu, L. Zhang, and R. S. M. Goh(2019), Traffic pattern mining and forecasting technologies in maritime traffic service networks: A comprehensive survey. IEEE Transactions on Intelligent Transportation Systems, 21(5), pp. 1796-1825. https://doi.org/10.1109/TITS.2019.2908191
  55. Xiao, Z., X. Fu, L. Zhang, L. Ponnambalam, and R. S. M. Goh(2017), Data-driven multi-agent system for maritime traffic safety management. Paper presented at the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1-6.
  56. Xin, X., K. Liu, X. Yang, Z. Yuan, and J. Zhang(2019), A simulation model for ship navigation in the "Xiazhimen" waterway based on statistical analysis of AIS data. Ocean Engineering, 180, pp. 279-289. https://doi.org/10.1016/j.oceaneng.2019.03.052
  57. Xue, J., P. Van Gelder, G. Reniers, E. Papadimitriou, and C. Wu(2019), Multi-attribute decision-making method for prioritizing maritime traffic safety influencing factors of autonomous ships' maneuvering decisions using grey and fuzzy theories. Safety Science, 120, pp. 323-340. https://doi.org/10.1016/j.ssci.2019.07.019
  58. Yang, D., A. T. Chin, and S. Chen(2014), Impact of politics, economic events and port policies on the evolution of maritime traffic in chinese ports. Maritime Policy and Management, 41(4), pp. 346-366. https://doi.org/10.1080/03088839.2013.784399
  59. Zhen, R., M. Riveiro, and Y. Jin(2017), A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, pp. 492-501. https://doi.org/10.1016/j.oceaneng.2017.09.015
  60. Zhang, W., X. Feng, F. Goerlandt, and Q. Liu(2020), Towards a convolutional neural network model for classifying regional ship collision risk levels for waterway risk analysis. Reliability Engineering and System Safety, 204, 107127. https://doi.org/10.1016/j.ress.2020.107127
  61. Zhou, Y., W. Daamen, T. Vellinga, and S. Hoogendoorn (2019), Review of maritime traffic models from vessel behavior modeling perspective. Transportation Research Part C: Emerging Technologies, 105, pp. 323-345. https://doi.org/10.1016/j.trc.2019.06.004
  62. Zissis, D., K. Chatzikokolakis, G. Spiliopoulos, and M. Vodas(2020), A distributed spatial method for modeling maritime routes. IEEE Access, 8, pp. 47556-47568. https://doi.org/10.1109/ACCESS.2020.2979612