IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0175840.html
   My bibliography  Save this article

Hierarchical and coupling model of factors influencing vessel traffic flow

Author

Listed:
  • Zhao Liu
  • Jingxian Liu
  • Huanhuan Li
  • Zongzhi Li
  • Zhirong Tan
  • Ryan Wen Liu
  • Yi Liu

Abstract

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

Suggested Citation

  • Zhao Liu & Jingxian Liu & Huanhuan Li & Zongzhi Li & Zhirong Tan & Ryan Wen Liu & Yi Liu, 2017. "Hierarchical and coupling model of factors influencing vessel traffic flow," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    • Handle: RePEc:plo:pone00:0175840
    DOI: 10.1371/journal.pone.0175840
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175840
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0175840&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0175840?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Maher Agi & Rohit Nishant, 2017. "Understanding influential factors on implementing green supply chain management practices: An interpretive structural modelling analysis," Post-Print hal-02005891, HAL.
    2. Goerlandt, Floris & Kujala, Pentti, 2011. "Traffic simulation based ship collision probability modeling," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 91-107.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcela Marçal Alves Pinto & João Luiz Kovaleski & Rui Tadashi Yoshino & Regina Negri Pagani, 2019. "Knowledge and Technology Transfer Influencing the Process of Innovation in Green Supply Chain Management: A Multicriteria Model Based on the DEMATEL Method," Sustainability, MDPI, vol. 11(12), pages 1-33, June.
    2. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    3. Denias Kagande & David Madzikanda & Maxwell Sandada & Faustino Taderera, 2022. "Barriers to Effective Supply Chain Management Implementation in the Zimbabwean Public Sector: A Case Study of Public Procuring Entities in Harare, Zimbabwe," Eurasian Journal of Business and Management, Eurasian Publications, vol. 10(2), pages 76-100.
    4. Naim Ahmad & Ayman Qahmash, 2021. "SmartISM: Implementation and Assessment of Interpretive Structural Modeling," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
    5. Dinesh Seth & Minhaj Ahemad A. Rehman, 2022. "Critical success factors‐based strategy to facilitate green manufacturing for responsible business: An application experience in Indian context," Business Strategy and the Environment, Wiley Blackwell, vol. 31(7), pages 2786-2806, November.
    6. Goerlandt, Floris & Montewka, Jakub, 2015. "Maritime transportation risk analysis: Review and analysis in light of some foundational issues," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 115-134.
    7. Szlapczynski, Rafal & Szlapczynska, Joanna, 2021. "A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Montewka, Jakub & Ehlers, Sören & Goerlandt, Floris & Hinz, Tomasz & Tabri, Kristjan & Kujala, Pentti, 2014. "A framework for risk assessment for maritime transportation systems—A case study for open sea collisions involving RoPax vessels," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 142-157.
    9. Saghiri, Soroosh Sam & Mirzabeiki, Vahid, 2021. "Buyer-led environmental supplier development: Can suppliers really help it?," International Journal of Production Economics, Elsevier, vol. 233(C).
    10. Mokhtar, Ahmad Rais Mohamad & Genovese, Andrea & Brint, Andrew & Kumar, Niraj, 2019. "Supply chain leadership: A systematic literature review and a research agenda," International Journal of Production Economics, Elsevier, vol. 216(C), pages 255-273.
    11. Piera Centobelli & Roberto Cerchione & Emilio Esposito, 2018. "Environmental Sustainability and Energy-Efficient Supply Chain Management: A Review of Research Trends and Proposed Guidelines," Energies, MDPI, vol. 11(2), pages 1-36, January.
    12. Maria Raimondo & Francesco Caracciolo & Luigi Cembalo & Gaetano Chinnici & Biagio Pecorino & Mario D’Amico, 2018. "Making Virtue Out of Necessity: Managing the Citrus Waste Supply Chain for Bioeconomy Applications," Sustainability, MDPI, vol. 10(12), pages 1-20, December.
    13. Shelmerdine, Richard L., 2015. "Teasing out the detail: How our understanding of marine AIS data can better inform industries, developments, and planning," Marine Policy, Elsevier, vol. 54(C), pages 17-25.
    14. Jin, Cheng-Jie & Yang, Wenzhang & Jiang, Rui & Liao, Peng & Zheng, Shiteng & Wang, Hao, 2023. "Vessel-following dynamics: Experiment and modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    15. Antonio Di Crescenzo & Barbara Martinucci & Paola Paraggio, 2023. "Vessels Arrival Process and its Application to the SHIP/M/ $$\infty$$ ∞ Queue," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-33, March.
    16. Lu, Qihui & Liao, Changhua & Shi, Victor & Xu, Zeping, 2024. "Implementation mode selection for blockchain technology in green product traceability," International Journal of Production Economics, Elsevier, vol. 276(C).
    17. Kamala kanta Muduli & Sunil Luthra & Sachin Kumar Mangla & Charbel Jose Chiappetta Jabbour & Satyabrata Aich & Julio Cesar Ferro de Guimarães, 2020. "Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains," Business Strategy and the Environment, Wiley Blackwell, vol. 29(4), pages 1647-1665, May.
    18. Haapasaari, Päivi & Helle, Inari & Lehikoinen, Annukka & Lappalainen, Jouni & Kuikka, Sakari, 2015. "A proactive approach for maritime safety policy making for the Gulf of Finland: Seeking best practices," Marine Policy, Elsevier, vol. 60(C), pages 107-118.
    19. Syed Shakil Ahmed & Tauhima Akter & Yuchao Ma, 2018. "Green Supply Chain Management (GSCM) Performance Implemented by the Textile Industry of Gazipur District, Dhaka," Logistics, MDPI, vol. 2(4), pages 1-18, September.
    20. Gino J. Lim & Jaeyoung Cho & Selim Bora & Taofeek Biobaku & Hamid Parsaei, 2018. "Models and computational algorithms for maritime risk analysis: a review," Annals of Operations Research, Springer, vol. 271(2), pages 765-786, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0175840. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.