Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-19T15:19:31.256Z Has data issue: false hasContentIssue false
  • English
  • Français

Modelling liquefied natural gas ship traffic in port based on cellular automaton and multi-agent system

Published online by Cambridge University Press:  09 March 2021

Jingxian Liu ,
Yang Liu  and
Le Qi Open the ORCID record for Le Qi [Opens in a new window]
Jingxian Liu
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China
Yang Liu
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China
Le Qi*
Affiliation:
School of Navigation, Wuhan University of Technology, Wuhan, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China
*
*Corresponding author. E-mail: leqiem@hotmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Over the past few decades, the number of liquefied natural gas (LNG) ships and terminals has been increasing, playing an important role in global clean energy transportation. However, the traffic capacity of LNG shipping in port areas is limited because of its high safety requirements. In view of this problem, a novel model is proposed to study the ship traffic in a port area by combining cellular automaton (CA) and multi-agent methods. Taking the CNOOC Tianjin LNG Terminal as an example, the ship traffic in Tianjin Port is simulated. Based on the simulation results, the LNG ship traffic capacity and its impact on the general shipping traffic flow under different special traffic rules are obtained. This model can provide theoretical support for optimising the port traffic organisation for LNG ships.

Keywords

LNG shipcellular automatonmulti-agenttraffic flow simulation
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 3 , May 2021 , pp. 533 - 548
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2021

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

Bush, A., Biles, E. W. and DePuy, G. W. (2003). Waterway, Shipping, and Ports: Iterative Optimization and Simulation of Barge Traffic on an Inland Waterway. Proceedings of the 35th Conference on Winter Simulation: Driving Innovation. New Orleans, Louisiana, USA, 1751–1756.Google Scholar
Chen, J., Zhang, W., Wan, Z., Li, S., Huang, T. and Fei, Y. (2019). Oil spills from global tankers: Status review and future governance. Journal of Cleaner Production, 227, 20–32.CrossRefGoogle Scholar
Chen, L., Hopman, H. and Negenborn, R. R. (2018). Distributed model predictive control for vessel train formations of cooperative multi-vessel systems. Transportation Research Part C: Emerging Technologies, 92, 101–118.CrossRefGoogle Scholar
Cortés, P., Muñuzuri, J., Ibáñez, J. N. and Guadix, J. (2007). Simulation of freight traffic in the Seville inland port. Simulation Modelling Practice and Theory, 15(3), 256–271.CrossRefGoogle Scholar
Dong, Y., Pei, W., Liu, G., Jin, L. and Chen, D. (2014). In-situ experimental and numerical investigation on the cooling effect of a multi-lane embankment with combined crushed-rock interlayer and ventilated ducts in permafrost regions. Cold Regions Science and Technology, 104, 97–105.CrossRefGoogle Scholar
Eldemir, F., Camci, F. and Uysal, O. (2013). Analysis and simulation of Istanbul Strait marine traffic management strategies (No. 13-1024). Transportation Research Board 92nd Annual Meeting. Washington DC, USA.Google Scholar
Fang, Z., Li, F., Li, R., Zhou, Z., Liu, G., Wen, J. and Zheng, Y. (2014). Modeling Multi-Lane Traffic Flow Under Different Overtaking Rules Based on Cellular Automaton. 2014 9th International Conference on Computer Science & Education. IEEE, 647–653.CrossRefGoogle Scholar
Guo, Z., Wan, H., Zhao, Y., Wang, H. and Li, Z. (2013). Driving simulation study on speed-change lanes of the multi-lane freeway interchange. Procedia-Social and Behavioral Sciences, 96, 60–69.CrossRefGoogle Scholar
Hashemi, M. and Karimi, H. A. (2016). A weight-based map-matching algorithm for vehicle navigation in complex urban networks. Journal of Intelligent Transportation Systems, 20(6), 573–590.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, 2020, 249.Google Scholar
Huang, C., Hu, B., Jiang, G. and Yang, R. (2016). Modeling of agent-based complex network under cyber-violence. Physica A: Statistical Mechanics and its Applications, 458, 399–411.CrossRefGoogle Scholar
Inaishi, M. (2004). A Ship Behavior Cluster Model for Maneuverability and Marine Traffic. Proceedings 2004 Hawaii International Conference on Computer Sciences. IEEE Computer Society, Washington DC, USA.Google Scholar
Jiang, L., Huang, G., Huang, C. and Wang, W. (2019). Data mining and optimization of a port vessel behavior behavioral model under the internet of things. IEEE Access, 7, 139970–139983.CrossRefGoogle Scholar
Ma, K., Yan, B. and Luo, X. (2014). A Cellular Automata Simulation for Traffic Flow on Multi-Lane Freeways Under Various Control Rules. Proceedings of the 11th World Congress on Intelligent Control and Automation. IEEE, 455–460.CrossRefGoogle Scholar
Mavrakis, D. and Kontinakis, N. (2008). A queueing model of maritime traffic in Bosporus Straits. Simulation Modelling Practice and Theory, 16(3), 315–328.CrossRefGoogle Scholar
Merrick, J. R. W., van Dorp, J. R., Blackford, J. P., Shaw, G. L., Harrald, J. and Mazzuchi, T. A. (2003). A traffic density analysis of proposed ferry service expansion in San Francisco Bay using a maritime simulation model. Reliability Engineering & System Safety, 81(2), 119–132.CrossRefGoogle Scholar
Na, U. J. and Shinozuka, M. (2009). Simulation-based seismic loss estimation of seaport transportation system. Reliability Engineering & System Safety, 94(3), 722–731.CrossRefGoogle Scholar
Nagel, K. and Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique I, 2(12), 2221–2229.CrossRefGoogle Scholar
Numano, M., Itoh, H. and Niwa, Y. (2001). Sea Traffic Simulation and its Visualization in Multi-PC System. Proc. International Congress on Modeling and Simulation 2001, December 2001, Canberra, Australia, 2093–2098.Google Scholar
Özbaş, B. and Or, I. (2007). Analysis and control of maritime transit traffic through the Istanbul Channel: A simulation approach. Central European Journal of Operations Research, 15(3), 235–252.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.CrossRefGoogle 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(9), 8430–8438.CrossRefGoogle Scholar
Sun, Z., Chen, Z., Hu, H. and Zheng, J. (2015). Vessel interaction in narrow water channels: A two-lane cellular automata approach. Physica A, 431, 46–51.CrossRefGoogle Scholar
Valdivia, J. A. (2015). Modeling a bus through a sequence of traffic lights. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(7), 073117.Google Scholar
Vaněk, O., Jakob, M., Hrstka, O. and Pěchouček, M. (2011). Using multi-agent simulation to improve the security of maritime transit. In: Villatoro, D., Sabater-Mir, J. and Sichman, J.S. (eds) International Workshop on Multi-Agent Systems and Agent-Based Simulation. Springer, Berlin and Heidelberg, 44–58.Google Scholar
Vaněk, O., Jakob, M., Hrstka, O. and Pěchouček, M. (2013). Agent-based model of maritime traffic in piracy-affected waters. Transportation Research Part C: Emerging Technologies, 36, 157–176. doi:10.1016/j.trc.2013.08.009CrossRefGoogle Scholar
Wagner, P., Nagel, K. and Wolf, D. E. (1997). Realistic multi-lane traffic rules for cellular automata. Physica A: Statistical Mechanics and its Applications, 234(3), 687–698.CrossRefGoogle Scholar
Wan, Z. and Chen, J. (2018). Human errors are behind most oil-tanker spills. Nature, 560, 161–163.CrossRefGoogle ScholarPubMed
Wooldridge, M. J., Weiß, G. and Paolo Ciancarini, P. (2002). Agent-oriented software engineering II. In: Lecture Notes in Computer Science, (Berlin, Heidelberg, Germany: Springer) 2222.Google Scholar
Wolfram, S. (1986). Random sequence generation by cellular automata. Advances in Applied Mathematics, 7(2), 123–169.CrossRefGoogle Scholar
Wooldridge, M. (2000). The Computational Complexity of Agent Design Problems. Proceedings Fourth International Conference on MultiAgent Systems. IEEE, 341–348.CrossRefGoogle 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
Zhang, F., Li, J. and Zhao, Q. (2005). Single-lane Traffic Simulation with Multi-Agent System. Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005. IEEE, 56–60.CrossRefGoogle Scholar
Zhu, H. J., Wang, D. and Zhou, J. B. (2014). A cellular automaton model for highway: Considering multi-lane traffic rules. In: Li, G., Chen, C., Jiang, B. and Shen, Q. (eds) Sustainable Cities Development and Environment Protection IV. Zurich, Switzerland: Trans Tech Publications Ltd. 2213–2219.Google Scholar
Zhuo, S., Zhonglong, C., Hongtao, H. and Zheng, J.-F. (2015). Ship interaction in narrow water channels: A two-lane cellular automata approach. Physica A: Statistical Mechanics and its Applications, 431, 46–51.Google Scholar