Hostname: page-component-6bf8c574d5-9nwgx Total loading time: 0 Render date: 2025-02-16T22:42:03.798Z Has data issue: false hasContentIssue false
  • English
  • Français

A review of ship collision risk assessment, hotspot detection and path planning for maritime traffic control in restricted waters

Published online by Cambridge University Press:  07 February 2023

Hongchu Yu Open the ORCID record for Hongchu Yu [Opens in a new window] ,
Qiang Meng ,
Zhixiang Fang ,
Jingxian Liu  and
Lei Xu
Hongchu Yu*
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan 430063, China Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya, China
Qiang Meng
Affiliation:
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
Zhixiang Fang
Affiliation:
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Jingxian Liu
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan 430063, China Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya, China
Lei Xu
Affiliation:
National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The three research topics, ship collision risk assessment, ship traffic hotspot detection and prediction, and collision-avoidance based ship path planning, are vital for next-generation vessel traffic management and monitoring systems. The system development is closely related to big data analytics and artificial intelligence for restricted waters. This study, therefore, aims to analyse the state-of-the art of these three topics over the latest decade, identify research gaps, and shed light on future research avenues. To achieve these three objectives, we critically and systematically review related articles that were published during the period between 2011 and 2021. We believe that this comprehensive and critical literature review would have a significant and profound impact on the formal safety assessment and vessel traffic management, and monitoring studies because it is not only an extension but also an essential continuity work of the literature review on maritime waterway risk assessment and prediction, as well as ship path guidance for ship collision risk mitigation in accordance with current automation vessels development and modern intelligent port construction.

Keywords

ship collision riskpath planningtraffic hotspot detectionrestricted waters
Type
Review Article
Information
The Journal of Navigation , Volume 75 , Issue 6 , November 2022 , pp. 1337 - 1363
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altan, Y. C. and Otay, E. N. (2018). Spatial mapping of encounter probability in congested waterways using AIS. Ocean Engineering, 164, 263–271.CrossRefGoogle Scholar
Ampah, J. D., Yusuf, A. A., Afrane, S., Jin, C. and Liu, H. (2021). Reviewing two decades of cleaner alternative marine fuels: towards IMO's decarbonization of the maritime transport sector. Journal of Cleaner Production, 320, 128871.CrossRefGoogle Scholar
Arima, T., Harada, S., Rahim, A. M. and Uraguchi, Y. (2008). Trial application of FE simulation on ships collision within the risk assessment on oil spills from oil tankers. ClassNK Technical Bulletin, 26, 29–34.Google Scholar
Asmara, I. P. S., Kobayashi, E., Artana, K. B., Masroeri, A. A. and Wakabayashi, N. (2014). Simulation-Based Estimation of Collision Risk During Ship Maneuvering in Two-Lane Canal Using Mathematical Maneuvering Group Model and Automatic Identification System Data. In International Conference on Offshore Mechanics and Arctic Engineering, Vol. 45424. American Society of Mechanical Engineers, V04AT02A055.CrossRefGoogle Scholar
Bukhari, A. C., Tusseyeva, I. and Kim, Y. G. (2013). An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system. Expert Systems with Applications, 40(4), 1220–1230.CrossRefGoogle Scholar
Candeloro, M., Lekkas, A. M. and Sørensen, A. J. (2017). A Voronoi-diagram-based dynamic path-planning system for underactuated marine vessels. Control Engineering Practice, 61, 41–54.CrossRefGoogle Scholar
Chen, S., Ahmad, R., Lee, B. G. and Kim, D. (2014). Composition ship collision risk based on fuzzy theory. Journal of Central South University, 21(11), 4296–4302.CrossRefGoogle Scholar
Chen, L., Negenborn, R. R. and Lodewijks, G. (2016). Path Planning for Autonomous Inland Vessels Using A* BG. In International Conference on Computational Logistics. Cham: Springer, 65–79.CrossRefGoogle Scholar
Chen, P., Mou, J. and van Gelder, P. H. A. J. M. (2017). Risk assessment methods for ship collision in estuarine waters using AIS and historical accident data. Maritime Transportation and Harvesting of Sea Resources, 213–221.Google Scholar
Chen, C., Chen, X. Q., Ma, F., Zeng, X. J. and Wang, J. (2019a). A knowledge-free path planning approach for smart ships based on reinforcement learning. Ocean Engineering, 189, 106299.CrossRefGoogle Scholar
Chen, P., Huang, Y., Mou, J. and van Gelder, P. H. A. J. M. (2019b). Probabilistic risk analysis for ship-ship collision: state-of-the-art. Safety Science, 117, 108–122.CrossRefGoogle Scholar
Christian, R. and Kang, H. G. (2017). Probabilistic risk assessment on maritime spent nuclear fuel transportation (part II: ship collision probability). Reliability Engineering & System Safety, 164, 136–149.CrossRefGoogle Scholar
COWI, A. (2008). Risk Analysis of Sea Traffic in the Area around Bornholm. Kongens Lyngby, Denmark: COWI AS. Report# P-65775-002.Google Scholar
Davis, P. V., Dove, M. J. and Stockel, C. T. (1982). A computer simulation of multi-ship encounters. The Journal of Navigation, 35(2), 347–352.CrossRefGoogle Scholar
Deng, W., Gan, L., Zhou, C., Zheng, Y., Liu, M. and Zhang, L. (2017). Study on Path Planning of Ship Collision Avoidance in Restricted Water Based on AFS Algorithm. In The 27th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers.Google Scholar
Dong, Y. and Frangopol, D. M. (2015). Probabilistic ship collision risk and sustainability assessment considering risk attitudes. Structural Safety, 53, 75–84.CrossRefGoogle Scholar
Fang, Z., Yu, H., Ke, R., Shaw, S. L. and Peng, G. (2018a). 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.CrossRefGoogle Scholar
Fang, Z., Yu, H., Lu, F., Feng, M. and Huang, M. (2018b). Maritime network dynamics before and after international events. Journal of Geographical Sciences, 28(7), 937–956.CrossRefGoogle Scholar
Fu, M. Y., Wang, S. S. and Wang, Y. H. (2019). Multi-behavior fusion based potential field method for path planning of unmanned surface vessel. China Ocean Engineering, 33(5), 583–592.CrossRefGoogle Scholar
Fujii, Y. and Shiobara, R. (1971). The analysis of traffic accidents. The Journal of Navigation, 24(4), 534–543.CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. The Journal of Navigation, 24(4), 543–552.CrossRefGoogle Scholar
Gang, L., Wang, Y., Sun, Y., Zhou, L. and Zhang, M. (2016). Estimation of vessel collision risk index based on support vector machine. Advances in Mechanical Engineering, 8(11), 1687814016671250.CrossRefGoogle Scholar
Goerlandt, F. and Kujala, P. (2014). On the reliability and validity of ship–ship collision risk analysis in light of different perspectives on risk. Safety Science, 62, 348–365.CrossRefGoogle Scholar
Goerlandt, F., Montewka, J. and Kujala, P. (2014). Tools for an Extended Risk Assessment for RoPax Ship-Ship Collision. In Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management, 2292–2302.CrossRefGoogle Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of Navigation, 28(3), 328–344.CrossRefGoogle Scholar
Hampel, B. (1985). The minimum manoeuvering range as a criterion to evaluate the risk of collision between ships. In: Heller, M. R. (ed.). Maritime Simulation. Berlin, Heidelberg: Springer, 242–253.CrossRefGoogle Scholar
Hinostroza, M. A., Xu, H. T. and Guedes Soares, C. (2017). Path-planning and path following control system for autonomous surface vessel. Maritime Transportation and Harvesting of Sea Resources, 991–998.Google Scholar
Huang, Y. and Van Gelder, P. H. A. J. M. (2019). Measuring ship collision risk in a dense traffic environment. TransNav, International Journal on Marine Navigation and Safety od Sea Transportation, 13(4), 737–744.CrossRefGoogle Scholar
Huang, Y., Van Gelder, P. H. A. J. M. and Mendel, M. B. (2016). Imminent Ships Collision Risk Assessment Based on Velocity Obstacle. In ESREL, 693–700.CrossRefGoogle Scholar
Im, N. and Luong, T. N. (2019). Potential risk ship domain as a danger criterion for real-time ship collision risk evaluation. Ocean Engineering, 194, 106610.CrossRefGoogle Scholar
Iphar, C., Napoli, A. and Ray, C. (2020). An expert-based method for the risk assessment of anomalous maritime transportation data. Applied Ocean Research, 104, 102337.CrossRefGoogle Scholar
Jinyu, L., Lei, L., Xiumin, C., Wei, H., Xinglong, L. and Cong, L. (2021). Automatic identification system data-driven model for analysis of ship domain near bridge-waters. The Journal of Navigation, 74(6), 1–22.CrossRefGoogle Scholar
Kaminski, R. and Smierzchalski, R. (2001). An evolutionary method of ship's anti-collision trajectory planning: new experiments. WIT Transactions on The Built Environment, 53, 125–132.Google Scholar
Kang, Y. T., Chen, W. J., Zhu, D. Q., Wang, J. H. and Xie, Q. M. (2018a). Collision avoidance path planning for ships by particle swarm optimization. Journal of Marine Science and Technology, 26(6), 777–786.Google Scholar
Kang, L., Meng, Q. and Liu, Q. (2018b). Fundamental diagram of ship traffic in the Singapore strait. Ocean Engineering, 147, 340–354.CrossRefGoogle Scholar
Kang, L., Lu, Z., Meng, Q., Gao, S. and Wang, F. (2019). Maritime simulator based determination of minimum DCPA and TCPA in head-on ship-to-ship collision avoidance in confined waters. Transportmetrica A: Transport Science, 15(2), 1124–1144.CrossRefGoogle Scholar
Kim, K. I. and Jeong, J. S. (2016). Visualization of Ship Collision Risk Based on Near-Miss Accidents. In 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 323–327CrossRefGoogle Scholar
Kołakowski, P., Ampah, J. D., Wróbel, K., Yusuf, A. A., Gil, M., Afrane, S., Jin, C., and Liu, H. (2022a). Alternative fuels in shipping: discussion on the findings of two recently published, independent bibliometric studies. Journal of Cleaner Production, 338, 130651.CrossRefGoogle Scholar
Kołakowski, P., Gil, M., Wróbel, K. and Ho, Y. S. (2022b). State of play in technology and legal framework of alternative marine fuels and renewable energy systems: a bibliometric analysis. Maritime Policy & Management, 49(2), 236–260.CrossRefGoogle Scholar
Kolendo, P. and Śmierzchalski, R. (2016). Experimental comparison of straight lines and polynomial interpolation modeling methods in ship evolutionary trajectory planning problem. In: Kacprzyk, J. (ed.). Advanced and Intelligent Computations in Diagnosis and Control. Cham: Springer, 331–340.CrossRefGoogle Scholar
Kuczkowski, Ł and Śmierzchalski, R. (2017). Path Planning Algorithm for Ship Collisions Avoidance in Environment with Changing Strategy of Dynamic Obstacles. In Polish Control Conference. Cham: Springer, 641–650.CrossRefGoogle Scholar
Lazarowska, A. (2015a). Parameters Influence on the Performance of an Ant Algorithm for Safe Ship Trajectory Planning. In 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). IEEE, 140–145.CrossRefGoogle Scholar
Lazarowska, A. (2015b). Ship's trajectory planning for collision avoidance at sea based on ant colony optimisation. The Journal of Navigation, 68(2), 291–307.CrossRefGoogle Scholar
Lazarowska, A. (2016). A trajectory base method for ship's safe path planning. Procedia Computer Science, 96, 1022–1031.CrossRefGoogle Scholar
Lazarowska, A. (2017). A new deterministic approach in a decision support system for ship's trajectory planning. Expert Systems with Applications, 71, 469–478.CrossRefGoogle Scholar
Lazarowska, A. (2018). A new Potential Field Inspired Path Planning Algorithm for Ships. In 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR). IEEE, 166–170.CrossRefGoogle Scholar
Lazarowska, A. (2020). A discrete artificial potential field for ship trajectory planning. The Journal of Navigation, 73(1), 233–251.CrossRefGoogle Scholar
Lee, H. J., Furukawa, Y. and Park, D. J. (2021). Seafarers’ awareness-based domain modelling in restricted areas. The Journal of Navigation, 74(5), 1172–1188.CrossRefGoogle Scholar
Li-Jia, C. and Li-Wen, H. (2012). Ship collision avoidance path planning by PSO based on maneuvering equation. In: Zhang, Y. (ed.). Future Wireless Networks and Information Systems, Berlin, Heidelberg: Springer, 675–682.Google Scholar
Li, B. and Pang, F. W. (2013). An approach of vessel collision risk assessment based on the D–S evidence theory. Ocean Engineering, 74, 16–21.CrossRefGoogle Scholar
Li, S., Meng, Q. and Qu, X. (2012). An overview of maritime waterway quantitative risk assessment models. Risk Analysis: An International Journal, 32(3), 496–512.CrossRefGoogle ScholarPubMed
Li, W., Ma, W., Yang, J., Shi, G. and Desrosiers, R. (2014). The Assessment of Risk of Collision Between two Ships Avoiding Collision by Altering Course. In International Conference on Algorithms and Architectures for Parallel Processing. Cham: Springer, 507–515.CrossRefGoogle Scholar
Li, Y., Guo, Z., Yang, J., Fang, H. and Hu, Y. (2018). Prediction of ship collision risk based on CART. IET Intelligent Transport Systems, 12(10), 1345–1350.CrossRefGoogle Scholar
Li, S., Liu, J., Wang, X., Guan, H. and Yan, Q. (2019a). Optimizing the Anti-Collision Decisions Between Multiple Ships Based on Predictive Risk Evaluations. In 2019 5th International Conference on Transportation Information and Safety (ICTIS). IEEE, 1395–1399.CrossRefGoogle Scholar
Li, J., Wang, H., Zhao, W. and Xue, Y. (2019b). Ship's trajectory planning based on improved multiobjective algorithm for collision avoidance. Journal of Advanced Transportation, 2019, 1–12.Google Scholar
Li, J., Wang, H., Guan, Z. and Pan, C. (2020). Distributed multi-objective algorithm for preventing multi-ship collisions at sea. The Journal of Navigation, 73(5), 971–990.CrossRefGoogle Scholar
Lin, X. and Yuan, Y. (2018). Algorithm of Ship Collision Risk Based on Improved BP Neural Network. In 2018 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 57–61.CrossRefGoogle Scholar
Lisowski, J. and Seghir, M. M. (1970). The safe ship control with minimum risk of collision. WIT Transactions on Ecology and the Environment, 31, 125–134.Google Scholar
Liu, H. and Liu, S. (2016). Application of grey relational decision-making on determination of ship collision risk degree. Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia., 39(3), 359–365.Google Scholar
Liu, J., Zhou, F., Li, Z., Wang, M. and Liu, R. W. (2016). Dynamic ship domain models for capacity analysis of restricted water channels. The Journal of Navigation, 69(3), 481–503.CrossRefGoogle Scholar
Liu, Z., Wu, Z. and Zheng, Z. (2019a). A cooperative game approach for assessing the collision risk in multi-vessel encountering. Ocean Engineering, 187, 106175.CrossRefGoogle Scholar
Liu, Z., Wu, Z. and Zheng, Z. (2019b). A novel framework for regional collision risk identification based on AIS data. Applied Ocean Research, 89, 261–272.CrossRefGoogle Scholar
Liu, K., Yu, Q., Yuan, Z., Yang, Z. and Shu, Y. (2021). A systematic analysis for maritime accidents causation in Chinese coastal waters using machine learning approaches. Ocean & Coastal Management, 213, 105859.CrossRefGoogle Scholar
Lu, Z., Kang, L., Gao, S. and Meng, Q. (2018). Determination of minimum distance to obstacle avoidance in the Singapore strait. Transportation Research Record, 2672(11), 73–80.CrossRefGoogle Scholar
Luo, M. and Shin, S. H. (2019). Half-century research developments in maritime accidents: future directions. Accident Analysis & Prevention, 123, 448–460.CrossRefGoogle ScholarPubMed
Luong, T. N., Hwang, S. and Im, N. (2021). Harbour traffic hazard map for real-time assessing waterway risk using marine traffic hazard index. Ocean Engineering, 239, 109884.CrossRefGoogle Scholar
Lyu, H. and Yin, Y. (2017). Ship's Trajectory Planning for Collision Avoidance at sea Based on Modified Artificial Potential Field. In 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 351–357.CrossRefGoogle Scholar
Lyu, H. and Yin, Y. (2019). COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields. The Journal of Navigation, 72(3), 588–608.CrossRefGoogle Scholar
McKay, M. D., Beckman, R. J. and Conover, W. J. (2000). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 42(1), 55–61.CrossRefGoogle Scholar
Mei, J. H. and Arshad, M. R. (2015). A hybrid artificial potential field method for autonomous surface vessel path planning in dynamic riverine environment. Indian Journal of Geo-Marine Sciences, 44(12), 1980–1994.Google Scholar
Mei, J. H., Arshad, M. R. and Tang, J. R. (2019). Collision risk assessment based artificial potential field approach for multi-ships avoidance. Indian Journal of Geo-Marine Sciences, 48(7), 1037–1047.Google Scholar
Meng, Q., Weng, J. and Li, S. (2014). Analysis with automatic identification system data of vessel traffic characteristics in the Singapore strait. Transportation Research Record, 2426(1), 33–43.CrossRefGoogle Scholar
Miyake, R., Fukuto, J. and Hasegawa, K. (2017). Evaluation Method of Collision Risk Based on Actual Ship Behaviours Extracted From AIS Data. In Marine Navigation CRC Press, 161–166.CrossRefGoogle Scholar
Mohamed-Seghir, M. (2016). Computational Intelligence Method for Ship Trajectory Planning. In 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 636–640.CrossRefGoogle Scholar
Mohamed-Seghir, M., Kula, K. and Kouzou, A. (2021). Artificial intelligence-based methods for decision support to avoid collisions at Sea. Electronics, 10(19), 2360.CrossRefGoogle Scholar
Montewka, J., Goerlandt, F., Innes-Jones, G., Owen, D., Hifi, Y. and Porthin, M. (2014a). Quantifying the Effect of Noise, Vibration and Motion on Human Performance in Ship Collision and Grounding Risk Assessment. In PSAM 12th Probabilistic Safety Assessment and Management Conference.Google Scholar
Montewka, J., Ehlers, S., Goerlandt, F., Hinz, T., Tabri, K. and Kujala, P. (2014b). A framework for risk assessment for maritime transportation systems—a case study for open sea collisions involving RoPax vessels. Reliability Engineering & System Safety, 124, 142–157.CrossRefGoogle Scholar
Namgung, H. and Kim, J. S. (2021). Regional collision risk prediction system at a collision area considering spatial pattern. Journal of Marine Science and Engineering, 9(12), 1365.CrossRefGoogle Scholar
Namgung, H., Jeong, J. S., Kim, J. S. and Kim, K. I. (2019). Inference model of collision risk Index based on artificial neural network using ship near-collision data. In Journal of Physics: Conference Series, 1357(1), 012044. IOP Publishing.Google Scholar
Nguyen, M., Zhang, S. and Wang, X. (2018). A novel method for risk assessment and simulation of collision avoidance for vessels based on AIS. Algorithms, 11(12), 204.CrossRefGoogle Scholar
Ni, S., Liu, Z., Cai, Y. and Wang, X. (2017). A Nonlinearity-Based Genetic Algorithm for Ship Path Planning. In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 1743–1748.CrossRefGoogle Scholar
Ni, S., Liu, Z., Cai, Y. and Wang, X. (2018). Modelling of ship's trajectory planning in collision situations by hybrid genetic algorithm. Polish Maritime Research, 25(3), 14–25.CrossRefGoogle Scholar
Pedersen, P. T. (1995). Collision and grounding mechanics. Proceedings of WEMT, 95(1995), 125–157.Google Scholar
Pietrzykowski, Z. (2008). Ship's fuzzy domain–a criterion for navigational safety in narrow fairways. The Journal of Navigation, 61(3), 499–514.CrossRefGoogle Scholar
Qiu, Y., Chen, W., Wang, W. and Zhang, G. (2010). A Model for Assessment Collision Risk Degree of Ships in Sight of Another. In ICCTP 2010: Integrated Transportation Systems: Green, Intelligent, Reliable, 867–874.CrossRefGoogle Scholar
Qu, X., Meng, Q. and Suyi, L. (2011). Ship collision risk assessment for the Singapore strait. Accident Analysis & Prevention, 43(6), 2030–2036.CrossRefGoogle ScholarPubMed
Qu, X., Meng, Q. and Li, S. (2012). Analyses and implications of accidents in Singapore strait. Transportation Research Record, 2273(1), 106–111.CrossRefGoogle Scholar
Ren, Y., Mou, J., Yan, Q. and Zhang, F. (2011). Study on Assessing Dynamic Risk of Ship Collision. In ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation, 2751–2757.CrossRefGoogle Scholar
Rong, H., Teixeira, A. P. and Soares, C. G. (2021). Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics. Reliability Engineering & System Safety, 209, 107463.CrossRefGoogle Scholar
Shen, Y., Wang, F., Zhao, P., Tong, X., Huang, J., Chen, K. and Zhang, H. (2019). Ship Route Planning Based on Particle Swarm Optimization. In 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 211–215.CrossRefGoogle Scholar
Shi, S., Zhang, D., Su, Y., Zhang, M., Sun, M. and Yao, H. (2019). Risk Factors Analysis Modeling for Ship Collision Accident in Inland River Based on Text Mining. In 2019 5th International Conference on Transportation Information and Safety (ICTIS). IEEE, 602–607.CrossRefGoogle Scholar
Shinoda, T. and Uru, K. (2016). Establishment of Risk Assessment Model for Ship Collision Using Applied Bayesian Network-Collision Accidents Between Fishing Vessels and Cargo Vessels. In 2015 SNAME Maritime Convention, SMC 2015. Society of Naval Architects and Marine Engineers, 207–217.Google Scholar
Shinoda, T. and Uryu, K. (2016). Risk Assessment Model for Ship Collisions in Consideration of Human Factors. In RINA, Royal Institution of Naval Architects-International Conference on Human Factors 2016. Royal Institution of Naval Architects.Google Scholar
Silveira, P. A. M., Teixeira, A. P. and Soares, C. G. (2013). Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. The Journal of Navigation, 66(6), 879.CrossRefGoogle Scholar
Smierzchalski, R. (1999a). An Intelligent Method of Ship's Trajectory Planning at sea. In Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No. 99TH8383). IEEE, 907–912.Google Scholar
Smierzchalski, R. (1999b). Evolutionary trajectory planning of ships in navigation traffic areas. Journal of Marine Science and Technology, 4(1), 1–6.CrossRefGoogle Scholar
Smierzchalski, R. (2000). Ships’ domains as a collision risk at sea in the evolutionary trajectory planning. WIT Transactions on Ecology and the Environment, 45, 43–52.Google Scholar
Śmierzchalski, R. (2005). Ships’ Domains as Collision Risk at sea in the Evolutionary Method of Trajectory Planning. In Information Processing and Security Systems. Boston, MA: Springer, 411–422.CrossRefGoogle Scholar
Soltan, R. A., Ashrafiuon, H. and Muske, K. R. (2011). ODE-based obstacle avoidance and trajectory planning for unmanned surface vessels. Robotica, 29(5), 691–703.CrossRefGoogle Scholar
Sotiralis, P., Ventikos, N. P., Hamann, R., Golyshev, P. and Teixeira, A. P. (2016). Incorporation of human factors into ship collision risk models focusing on human centred design aspects. Reliability Engineering & System Safety, 156, 210–227.CrossRefGoogle Scholar
Ståhlberg, K., Goerlandt, F., Ehlers, S. and Kujala, P. (2013). Impact scenario models for probabilistic risk-based design for ship–ship collision. Marine Structures, 33, 238–264.CrossRefGoogle Scholar
Svanberg, M., Santén, V., Hörteborn, A., Holm, H. and Finnsgård, C. (2019). AIS in maritime research. Marine Policy, 106, 103520.CrossRefGoogle Scholar
Szlapczynski, R. (2006). A unified measure of collision risk derived from the concept of a ship domain. The Journal of Navigation, 59(3), 477.CrossRefGoogle Scholar
Szłapczyński, R. (2012). Evolutionary approach to ship's trajectory planning within traffic separation schemes. Polish Maritime Research, 19, 11–20.CrossRefGoogle Scholar
Szłapczyński, R. and Śmierzchalski, R. (2009). Supporting navigator's decisions by visualizing ship collision risk. Polish Maritime Research, 16(1), 83–88.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2012). On evolutionary computing in multi-ship trajectory planning. Applied Intelligence, 37(2), 155–174.CrossRefGoogle Scholar
Tam, C. and Bucknall, R. (2010a). Collision risk assessment for ships. Journal of Marine Science and Technology, 15(3), 257–270.CrossRefGoogle Scholar
Tam, C. and Bucknall, R. (2010b). Path-planning algorithm for ships in close-range encounters. Journal of Marine Science and Technology, 15(4), 395–407.CrossRefGoogle Scholar
Tam, C. and Bucknall, R. (2013). Cooperative path planning algorithm for marine surface vessels. Ocean Engineering, 57, 25–33.CrossRefGoogle Scholar
Tan, X., Tao, J. and Konovessis, D. (2019). Preliminary design of a tanker ship in the context of collision-induced environmental-risk-based ship design. Ocean Engineering, 181, 185–197.CrossRefGoogle Scholar
Tang, K., Yang, Y., Zhao, M., Li, M. and Hao, Z. (2019). Cooperative Trajectory Planning and Trajectory Tracking Control for Multiple Surface Vessels System. In 2019 Chinese Control Conference (CCC). IEEE, 6100–6105.CrossRefGoogle Scholar
Teixeira, A. P. and Guedes Soares, C. (2018). Risk of Maritime Traffic in Coastal Waters. In International Conference on Offshore Mechanics and Arctic Engineering, Vol. 51326. American Society of Mechanical Engineers, p. V11AT12A025CrossRefGoogle Scholar
Tsou, M. C. and Hsueh, C. K. (2010). The study of ship collision avoidance route planning by ant colony algorithm. Journal of Marine Science and Technology, 18(5), 746–756.CrossRefGoogle Scholar
Tsou, M. C., Kao, S. L. and Su, C. M. (2010). Decision support from genetic algorithms for ship collision avoidance route planning and alerts. The Journal of Navigation, 63(1), 167–182.CrossRefGoogle Scholar
Van Nunen, K., Li, J., Reniers, G. and Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108, 248–258.CrossRefGoogle Scholar
Wang, N. (2010). An intelligent spatial collision risk based on the quaternion ship domain. Journal of Navigation, 63(4), 733–749.CrossRefGoogle Scholar
Wang, N. (2012). Intelligent quaternion ship domains for spatial collision risk assessment. Journal of Ship Research, 56(3), 170–182.CrossRefGoogle Scholar
Wang, Y., Huang, L. and Fan, Y. (2016). Research on Inland Ship Collision Risk Entropy Model-Based-Complex System. In The 26th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers.Google Scholar
Wang, T., Yan, X., Wang, Y. and Wu, Q. (2017). A Distributed Model Predictive Control Using Virtual Field Force for Multi-Ship Collision Avoidance Under COLREGS. In 2017 4th International Conference on Transportation Information and Safety (ICTIS). IEEE, pp. 296–305.CrossRefGoogle Scholar
Wang, Y., Huang, L., Xiong, X., Wang, M., Yuan, H. and Wang, L. (2018a). Collision Risk Prediction and Quantification Method for Multi-Ship Encountered Situation with Multi-Factors in Inland Water. In The 28th International Ocean and Polar Engineering Conference. International Society of Offshore and Polar Engineers.Google Scholar
Wang, C., Zhang, X., Li, R. and Dong, P. (2018b). Path Planning of Maritime Autonomous Surface Ships in Unknown Environment with Reinforcement Learning. In International Conference on Cognitive Systems and Signal Processing. Singapore: Springer, 127–137.CrossRefGoogle Scholar
Wang, N., Chang, D., Yuan, J., Shi, X. and Bai, X. (2020a). How to maintain the safety level with the increasing capacity of the fairway: a case study of the Yangtze estuary deepwater channel. Ocean Engineering, 216, 108122.CrossRefGoogle Scholar
Wang, Y. F., Wang, L. T., Jiang, J. C., Wang, J. and Yang, Z. L. (2020b). Modelling ship collision risk based on the statistical analysis of historical data: a case study in Hong Kong waters. Ocean Engineering, 197, 106869.CrossRefGoogle Scholar
Wang, X., Feng, K., Wang, G. and Wang, Q. (2021). Local path optimization method for unmanned ship based on particle swarm acceleration calculation and dynamic optimal control. Applied Ocean Research, 110, 102588.CrossRefGoogle Scholar
Wei, X. (2018). Risk assessment model of vessel collision based on fuzzy decision. Journal of Coastal Research, 83, 916–920.CrossRefGoogle Scholar
Weng, J., Meng, Q. and Qu, X. (2012). Vessel collision frequency estimation in the Singapore strait. The Journal of Navigation, 65(2), 207–221.CrossRefGoogle Scholar
Wennink, C. J. (1992). Collision and grounding risk analysis for ships navigating in confined waters. The Journal of Navigation, 45(1), 80–90.CrossRefGoogle Scholar
Witkowska, A., Śmierzchalski, R. and Wilczyński, P. (2017). Trajectory Planning for Service Ship During Emergency STS Transfer Operation. In Polish Control Conference. Cham, Springer, 514– 523.CrossRefGoogle Scholar
Wu, X., Mehta, A. L., Zaloom, V. A. and Craig, B. N. (2016). Analysis of waterway transportation in southeast Texas waterway based on AIS data. Ocean Engineering, 121, 196–209.CrossRefGoogle Scholar
Wu, B., Yip, T. L., Yan, X. and Mao, Z. (2020). A mutual information-based Bayesian network model for consequence estimation of navigational accidents in the Yangtze river. The Journal of Navigation, 73(3), 559–580.CrossRefGoogle Scholar
Xie, L., Xue, S., Zhang, J., Zhang, M., Tian, W. and Haugen, S. (2019). A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters. Ocean Engineering, 184, 311–322.CrossRefGoogle Scholar
Xu, H., Wang, N., Zhao, H. and Zheng, Z. (2019). Deep reinforcement learning-based path planning of underactuated surface vessels. Cyber-Physical Systems, 5(1), 1–17.CrossRefGoogle Scholar
Xu, L., Chen, N., Chen, Z., Zhang, C. and Yu, H. (2021). Spatiotemporal forecasting in earth system science: methods, uncertainties, predictability and future directions. Earth-Science Reviews. 222, 103828.CrossRefGoogle Scholar
Xue, Y. Z., Han, D. F. and Tong, S. Y. (2011). Automatic trajectory planning and collision avoidance of ships in confined waterways. Sustainable Maritime Transportation and Exploitation of Sea Resources, 167, 167–172.CrossRefGoogle Scholar
Yan, F., Zhao, L., Pan, M. and Inaishi, M. (2009). A Path Planning Algorithm for Autonomous Ship Agents. In International Conference on Transportation Engineering 2009. 1402–1407.CrossRefGoogle Scholar
Yang, G. and Yang, Z. (2013). Research of Computing the Vessels’ Collision Risk Index by Multiple Parameters Based on Neural Network with Genetic Algorithm. In 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control. IEEE, 712–714.CrossRefGoogle Scholar
Yoo, Y. and Lee, J. S. (2019). Evaluation of ship collision risk assessments using environmental stress and collision risk models. Ocean Engineering, 191, 106527.CrossRefGoogle Scholar
Yu, J., Deng, W., Zhao, Z., Wang, X., Xu, J., Wang, L., … and Shen, Z. (2019a). A hybrid path planning method for an unmanned cruise ship in water quality sampling. IEEE Access, 7, 87127–87140.CrossRefGoogle Scholar
Yu, H., Fang, Z., Murray, A. T. and Peng, G. (2019b). A direction-constrained space-time prism-based approach for quantifying possible multi-ship collision risks. IEEE Transactions on Intelligent Transportation Systems, 22(1), 131–141.CrossRefGoogle Scholar
Yu, H., Fang, Z., Lu, F., Murray, A. T., Zhang, H., Peng, P., … and Chen, J. (2019c). Impact of oil price fluctuations on tanker maritime network structure and traffic flow changes. Applied Energy, 237, 390–403.CrossRefGoogle Scholar
Yu, H., Murray, A. T., Fang, Z., Liu, J., Peng, G., Solgi, M. and Zhang, W. (2021a). Ship Path Optimization That Accounts for Geographical Traffic Characteristics to Increase Maritime Port Safety. IEEE Transactions on Intelligent Transportation Systems, 1–12.Google Scholar
Yu, H., Fang, Z., Fu, X., Liu, J. and Chen, J. (2021b). Literature review on emission control-based ship voyage optimization. Transportation Research Part D: Transport and Environment, 93, 102768.CrossRefGoogle Scholar
Yujie, L., Yu, P., Yixin, S., Huajun, Z., Danhong, Z. and Yong, S. (2018) Ship Path Planning Based on Improved Particle Swarm Optimization. In 2018 Chinese Automation Congress (CAC). IEEE, 226–230.CrossRefGoogle Scholar
Zaman, M. B., Kobayashi, E., Wakabayashi, N., Khanfir, S., Pitana, T. and Maimun, A. (2014). Fuzzy FMEA model for risk evaluation of ship collisions in the Malacca strait: based on AIS data. Journal of Simulation, 8(1), 91–104.CrossRefGoogle Scholar
Zhang, L. and Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, 106130.CrossRefGoogle Scholar
Zhang, W., Montewka, J. and Goerlandt, F. (2014). Semi-qualitative method for ship collision risk assessment. In: Mlynczak, M. and Nowakowski, T. (eds.). Safety and Reliability: Methodology and Applications. UK: CRC Press, 1599–1608.Google Scholar
Zhang, W., Goerlandt, F., Montewka, J. and Kujala, P. (2015a). A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107, 60–69.CrossRefGoogle Scholar
Zhang, L., Wang, H. and Meng, Q. (2015b). Big data–based estimation for ship safety distance distribution in port waters. Transportation Research Record, 2479(1), 16–24.CrossRefGoogle Scholar
Zhang, W., Feng, X., Qi, Y., Shu, F., Zhang, Y. and Wang, Y. (2019a). Towards a model of regional vessel near-miss collision risk assessment for open waters based on AIS data. The Journal of Navigation, 72(6), 1449–1468.CrossRefGoogle Scholar
Zhang, Q., Li, C., Lu, X. and Huang, S. (2019b). Research on Complete Coverage Path Planning for Unmanned Surface Vessel. In IOP Conference Series: Earth and Environmental Science, Vol. 300(2). IOP Publishing, 022037.CrossRefGoogle Scholar
Zhang, L., Meng, Q. and Fwa, T. F. (2019c). Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters. Transportation Research Part E: Logistics and Transportation Review, 129, 287–304.CrossRefGoogle Scholar
Zhang, L., Wang, H., Meng, Q. and Xie, H. (2019d). Ship accident consequences and contributing factors analyses using ship accident investigation reports. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 233(1), 35–47.Google Scholar
Zhang, J., He, A., Fan, C., Yan, X. and Soares, C. G. (2021). Quantitative analysis on risk influencing factors in the Jiangsu segment of the Yangtze river. Risk Analysis, 41(9), 1560–1578.CrossRefGoogle ScholarPubMed
Zhao, W., Wang, Y., Zhang, Z. and Wang, H. (2021). Multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm. Journal of Marine Science and Engineering, 9(4), 357.CrossRefGoogle Scholar
Zhen, R., Riveiro, M. and Jin, Y. (2017). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, 492–501.CrossRefGoogle Scholar
Zhen, R., Shi, Z., Shao, Z. and Liu, J. (2021). A novel regional collision risk assessment method considering aggregation density under multi-ship encounter situations. The Journal of Navigation, 75(1), 1–19.Google Scholar
Zhou, H., Zhao, D. and Guo, X. (2017). Global Path Planning of Unmanned Surface Vessel Based on Multi-Objective Hybrid Particle Swarm Algorithm. In International Conference on Bio-Inspired Computing: Theories and Applications. Singapore: Springer, 82–91.CrossRefGoogle Scholar
Zhou, X. Y., Liu, Z. J., Wang, F. W. and Ni, S. K. (2018). Collision Risk Identification of Autonomous Ships Based on the Synergy Ship Domain. In 2018 Chinese Control and Decision Conference (CCDC). IEEE, 6746–6752.CrossRefGoogle Scholar
Zhu, M., Wang, Y. and Wen, Y. (2013). A Global Path Planning Algorithm of Unmanned Vessel in Inland Waterway. In ICTIS 2013: Improving Multimodal Transportation Systems-Information, Safety, and Integration. 2106–2113.Google Scholar