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A cellular automaton-based model of ship traffic flow in busy waterways

Published online by Cambridge University Press:  08 January 2021

Le Qi Open the ORCID record for Le Qi [Opens in a new window] ,
Yuanyuan Ji ,
Robert Balling  and
Wenhai Xu
Le Qi*
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan430063, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan430063, China.
Yuanyuan Ji
Affiliation:
College of Information Science Technology, Dalian Maritime University, Dalian116026, China. School of Geographical Sciences and Urban Planning, Arizona State University, TempeAZ85281, USA
Robert Balling
Affiliation:
School of Geographical Sciences and Urban Planning, Arizona State University, TempeAZ85281, USA
Wenhai Xu
Affiliation:
College of Information Science Technology, Dalian Maritime University, Dalian116026, China.
*
*Corresponding author. E-mail: [email protected]
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Abstract

In busy waterways, spatial-temporal discretisation, safe distance and collision avoidance timing are three of the core components of ship traffic flow modelling based on cellular automata. However, these components are difficult to determine in ship traffic simulations because the size, operation and manoeuvrability vary between ships. To solve these problems, a novel traffic flow model is proposed. Firstly, a spatial-temporal discretisation method based on the concept of a standard ship is presented. Secondly, the update rules for ships’ motion are built by considering safe distance and collision avoidance timing, in which ship operation and manoeuvrability are thoroughly considered. We demonstrate the effectiveness of our model, which is implemented through simulating ship traffic flow in a waterway of the Yangtze River, China. By comparing the results with actual observed ship traffic data, our model shows that the behaviours and the characteristics of ships’ motions can be represented very well, which also can be further used to reveal the mechanism that affects the efficiency and safety of ship traffic.

Keywords

ship traffic flowcellular automatonsafe distancecollision avoidancestandard ship
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 3 , May 2021 , pp. 605 - 618
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Copyright
Copyright © The Royal Institute of Navigation 2021

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References

Bao, J., Chen, Q., Luo, D., Wu, Y. and Liang, Z. (2018). Exploring the impact of signal types and adjacent vehicles on drivers’ choices after the onset of yellow. Physica A: Statistical Mechanics and its Applications, 500, 222–236.10.1016/j.physa.2018.02.066CrossRefGoogle Scholar
Bell, M. G. and Meng, Q. (2016). Special issue in transportation research part B - shipping, port and maritime logistics. Transportation Research Part B: Methodological, 93, 697–699.CrossRefGoogle Scholar
Bi, X., Jia, C. and Wu, Z. (2004a). Opportunity and actions taken of ship's changing speed collision avoidance. Journal of Dalian Maritime University, 30, 26–28.Google Scholar
Bi, X., Shi, G., Jia, C. and Wu, Z. (2004b). Opportunity and actions taken of ship's changing speed collision avoidance. Journal of Zhanjiang Ocean University, 24, 37–40.Google Scholar
Biham, O., Middleton, A. A. and Levine, D. (1992). Self-organization and a dynamical transition in traffic-flow models. Physical Review A, 46, R6124.CrossRefGoogle Scholar
Blokus-Roszkowska, A. and Smolarek, L. (2013). Application of simulation methods for evaluating the sea waterways traffic organisation. ISRN Applied Mathematics, 9, 1541–1548.Google Scholar
Chen, J., Lei, H., Tian, S. and Ji-Cheng, H. E. (2009). Analysis for port traffic stream by cellular automaton with queuing theory. Journal of Shenyang Normal University, 27, 319–322.Google Scholar
Chen, J., Zhang, W., Wan, Z., Li, S., Huang, T. and Fei, Y. (2019a). Oil spills from global tankers: Status review and future governance. Journal of Cleaner Production, 227, 20–32.CrossRefGoogle Scholar
Chen, J., Zheng, T., Garg, A., Xu, L., Li, S. and Fei, Y. (2019b). Alternative maritime power application as a green port strategy: barriers in China. Journal of Cleaner Production, 213, 825–837.10.1016/j.jclepro.2018.12.177CrossRefGoogle Scholar
Chen, L. and Huang, L. (2012). Ship collision avoidance path planning by PSO based on maneuvering equation. Future Wireless Networks and Information Systems, Berlin, Heidelberg, Germany: Springer, 675–682.CrossRefGoogle Scholar
Chen, Q. and Wang, Y. (2015). Cellular automata (CA) simulation of the interaction of vehicle flows and pedestrian crossings on urban low-grade uncontrolled roads. Physica A: Statistical Mechanics and its Applications, 432, 43–57.CrossRefGoogle Scholar
Chen, Q. and Wang, Y. (2016). A cellular automata (CA) model for motorized vehicle flows influenced by bicycles along the roadside. Journal of Advanced Transportation, 50, 949–966.CrossRefGoogle Scholar
Chen, Q., Yi, J. and Wu, Y. (2019c). Cellular automaton simulation of vehicles in the contraflow left-turn lane at signalised intersections. IET Intelligent Transport Systems, 13, 1164–1172.CrossRefGoogle Scholar
Chowdhury, D., Santen, L. and Schadschneider, A. (2000). Statistical physics of vehicular traffic and some related systems. Physics Reports, 329, 199–329.CrossRefGoogle Scholar
Ding, J. X., Huang, H. J. and Tian, Q. (2009). A mixed traffic flow model based on a modified cellular automaton in two-lane system. Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, Sanya, Hainan, China: IEEE, 2, 124–126.CrossRefGoogle Scholar
Erwin, V. I. (2012). Detection of hazardous encounters at the North Sea from AIS data. Proceedings of International Workshop on Next Generation Nautical Traffic Models, 1–12.Google Scholar
Feng, H. (2013). Cellular automata ship traffic flow model considering integrated bridge system. International Journal of u-and e-Service, Science and Technology, 6, 111–120.CrossRefGoogle Scholar
Feng, H., Kong, F., Xiao, Y. and Yang, X. (2014a). Parameters characteristics analysis of ship traffic flow with cellular automata model on AIS-based. Journal of Wuhan University of Technology (Transportation Science, Engineering), 38, 324–328.Google Scholar
Feng, H., Kong, F., Xiao, Y. and Yang, X. (2014b). AIS-Based cellular automaton modeling of waterway moving bottleneck. Navigation of China, 37, 87–93.Google Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. Journal of Navigation, 24, 543–552.CrossRefGoogle Scholar
Goodwin, E. M. (1973). A statistical study of ship domains. Journal of Navigation, 26, 130–130.CrossRefGoogle Scholar
Hansen, M. G., Jensen, T. K., Lehn-Schiøler, T., Melchild, K., Rasmussen, F. M. and Ennemark, F. (2013). Empirical ship domain based on AIS data. Journal of Navigation, 66, 931–940.CrossRefGoogle Scholar
Helbing, D. (2001). Traffic and related self-driven many-particle systems. Reviews of Modern Physics, 73, 1067–1141.CrossRefGoogle Scholar
Hörteborn, A., Ringsberg, J. W., Svanberg, M. and Holm, H. (2019). A revisit of the definition of the ship domain based on AIS analysis. Journal of Navigation, 72, 777–794.CrossRefGoogle Scholar
Hua, C., Chen, J., Wan, Z., Xu, L., Bai, Y., Zheng, T. and Fei, Y. (2020). Evaluation and governance of green development practice of port: a sea port case of China. Journal of Cleaner Production, 249, 119434.CrossRefGoogle Scholar
Huang, Y., Yip, T. L. and Wen, Y. (2019). Comparative analysis of marine traffic flow in classical models. Ocean Engineering, 187, 106195.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, 1124–1144.CrossRefGoogle Scholar
Ke, J. (2010). Study on navigable hydro-junction organization model with cellular automata. Ph.D. thesis, Wuhan University of Technology.Google Scholar
Kerner, B. S., Klenov, S. L. and Wolf, D. E. (2002). Cellular automata approach to three-phase traffic theory. Journal of Physics A: Mathematical and General, 35, 9971–10013.10.1088/0305-4470/35/47/303CrossRefGoogle Scholar
Khan, B., Khan, F. and Veitch, B. (2019). A cellular automation model for convoy traffic in arctic waters. Cold Regions Science and Technology, 164, 102783.CrossRefGoogle Scholar
Lamb, W. G. P. (1983). The estimation of the mean size of ship domains. Journal of Navigation, 36, 130–136.10.1017/S0373463300028642CrossRefGoogle Scholar
Liao, P., Hou, M., Chu, M. and Zhang, W. (2019). Simulation of vessel traffic flow in inland waterway based on cellular automata. CICTP 2019, 1–10.CrossRefGoogle 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. Journal of Navigation, 69, 481–503.CrossRefGoogle Scholar
Liu, J., Zhou, F. and Wang, M. (2010). Simulation of waterway traffic flow at harbor based on the ship behavior and cellular automata. 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI), Vol. 3, 542–546.10.1109/AICI.2010.352CrossRefGoogle Scholar
Liu, S., Wang, N. and Wu, Z. (2011). Review of research on ship domain. Journal of Dalian Maritime University, 37, 51–54.Google Scholar
Nagel, K. and Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique I, 2, 2221–2229.CrossRefGoogle Scholar
Naito, Y. and Nagatani, T. (2012). Effect of headway and velocity on safety-collision transition induced by lane changing in traffic flow. Physica A: Statistical Mechanics and its Applications, 391, 1626–1635.CrossRefGoogle Scholar
Qi, L. and Ji, Y. (2020). Ship trajectory data compression algorithms for Automatic Identification System: Comparison and analysis. Journal of Water Resources and Ocean Science, 9, 42–47.CrossRefGoogle Scholar
Qi, L., Zheng, Z. and Gang, L. (2017a). A cellular automaton model for ship traffic flow in waterways. Physica A: Statistical Mechanics and its Applications, 471, 705–717.CrossRefGoogle Scholar
Qi, L., Zheng, Z. and Gang, L. (2017b). Marine traffic model based on cellular automaton: Considering the change of the ship's velocity under the influence of the weather and sea. Physica A: Statistical Mechanics and its Applications, 483, 480–494.10.1016/j.physa.2017.04.125CrossRefGoogle Scholar
Qi, L., Zheng, Z. and Li, G. (2011). AIS-data-based ship domain of ships in sight of one another. Journal of Dalian Maritime University, 37, 48–50.Google Scholar
Qu, X. and Meng, Q. (2012). Development and applications of a simulation model for vessels in the Singapore Straits. Expert Systems with Applications, 39, 8430–8438.10.1016/j.eswa.2012.01.176CrossRefGoogle Scholar
Rahman, M., Chowdhury, M., Xie, Y. C. and He, Y. M. (2013). Review of microscopic lane-changing models and future research opportunities. IEEE Transactions on Intelligent Transportation Systems, 14, 1942–1956.CrossRefGoogle Scholar
Rong, K. (2016). Port traffic Modeling and simulation based on cellular automaton. Master's thesis, Dalian Maritime University.Google Scholar
Sun, Z., Chen, Z., Hu, H. and Zheng, J. (2015). Ship interaction in narrow water channels: a two-lane cellular automata approach. Physica A: Statistical Mechanics and its Applications, 431, 46–51.CrossRefGoogle Scholar
Takayasu, M. and Takayasu, H. (1993). 1/f noise in a traffic model. Fractals, 1, 860–866.CrossRefGoogle Scholar
Tang, Y. (2016). Research on characteristics of vessel traffic flow on the grand canal. Master's thesis, Southeast University.Google Scholar
UNCTAD (2018). 50 years of review of maritime transport, 1968–2018, United Nations Conference on Trade and Development (UNCTAD), New York.Google Scholar
Wang, J. (2011). Research on intelligent traffic marine traffic simulation and quantitative assessment of the traffic environment. Master's thesis, Dalian Maritime University.Google Scholar
Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55, 601–644.CrossRefGoogle Scholar
Wolfram, S. (1984a). Cellular automata as models of complexity. Nature, 311, 419–424.CrossRefGoogle Scholar
Wolfram, S. (1984b). Universality and complexity in cellular automata. Physica D: Nonlinear Phenomena, 10, 1–35.CrossRefGoogle Scholar
Wolfram, S. and Mallinckrodt, A. J. (1995). Cellular automata and complexity. Computers in Physics, 9, 55–55.CrossRefGoogle Scholar
Wu, Z. and Zhu, J. (2004). Marine Traffic Engineering. Dalian: Dalian Maritime University Press.Google Scholar
Xiao, Z. (2011). Simulation and research on port traffic flow based on the theory of cellular automaton. Master's thesis, Harbin Engineering University.Google Scholar
Xin, X., Liu, K., Yang, X., Yuan, Z. and Zhang, J. (2019). A simulation model for ship navigation in the ‘Xiazhimen’ waterway based on statistical analysis of AIS data. Ocean Engineering, 180, 279–289.10.1016/j.oceaneng.2019.03.052CrossRefGoogle Scholar
Xu, W., Chu, X. and Liu, X. (2014). Review of modeling and simulation of vessel traffic flow. Hydro-science and Engineering, 6, 91–99.Google Scholar
Yan, F., Pan, M. Y., Wang, D. Q. and Gong, X. (2009). Marine traffic flow simulation based on combination of agent and cellular automata. Journal of Dalian Maritime University, 32, 89–92.Google Scholar
Yang, C., Chen, Q. and Chen, L. (2019). Modeling and simulation of following behaviors of pedestrians under limited visibility. Acta Physica Sinica, 68, 240504.CrossRefGoogle Scholar
Yang, F., Qiao, Y., Wei, W., Wang, X., Wan, D., Damaševičius, R. and Woźniak, M. (2020). Ddtree: A hybrid deep learning model for real-time waterway depth prediction and smart navigation. Applied Sciences, 10, 2770.CrossRefGoogle Scholar
Yi, J., Pan, S. and Chen, Q. (2020). Simulation of pedestrian evacuation in stampedes based on a cellular automaton model. Simulation Modelling Practice and Theory, 104, 102147.CrossRefGoogle Scholar
Yu, Y., El Kamel, A., Gong, G. and Li, F. (2014). Multi-agent based modeling and simulation of microscopic traffic in virtual reality system. Simulation Modelling Practice and Theory, 45, 62–79.10.1016/j.simpat.2014.04.001CrossRefGoogle Scholar
Zhao, J., Price, W. G., Wilson, P. A. and Tan, M. (1995). The uncertainty and uncoordination of mariners’ behaviour in collision avoidance at sea. Journal of Navigation, 48, 425–435.CrossRefGoogle Scholar
Zhao, J., Wu, Z. and Wang, F. (1993). Comments on ship domains. Journal of Navigation, 46, 422–436.Google Scholar
Zhao, Y. (2017). Key technologies research of main fairway VTS decision support of Xiamen port. Master's thesis, Jimei University.Google Scholar
Zhou, D. and Zheng, Z. (2019). Dynamic fuzzy ship domain considering the factors of own ship and other ships. Journal of Navigation, 72, 467–482.CrossRefGoogle Scholar
Zhou, Y., Daamen, W., Vellinga, T. and Hoogendoorn, S. (2019). Review of maritime traffic models from vessel behavior modeling perspective. Transportation Research Part C: Emerging Technologies, 105, 323–345.CrossRefGoogle Scholar
Zhuo, Y. and Tang, T. (2008). An intelligent decision support system to ship anti-collision in multi-ship encounter. 2008 7th World Congress on Intelligent Control and Automation, 1066–1071.CrossRefGoogle Scholar