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Modeling dynamic behavior of navigable inland waterways

Author

Listed:
  • Furkan Oztanriseven

    (Le Moyne College)

  • Heather Nachtmann

    (University of Arkansas)

Abstract

The inland waterway transportation system of the United States links its inland ports with the rest of the world by providing a cost-effective, fuel-efficient, and environmentally friendly mode of freight transportation. For this research study, a maritime transportation simulator (MarTranS) that integrates agent-based modeling, discrete-event simulation, system dynamics, and multiregional input–output analysis was developed to elucidate the relationships between inland waterway transportation system components and their associated economic impacts. An improved understanding of these relationships is critical to the assessment of risk reduction within the system, decisions concerning investment in support infrastructure, and future economic impacts. It is hypothesized that failure to invest in future improvements to the infrastructure that supports the inland waterway transportation system will have negative long-term economic consequences. While the performance of the MarTranS is demonstrated through a case study of the McClellan–Kerr Arkansas River Navigation System, the work is generalizable to other segments of the inland waterway transportation system and can enable maritime transportation stakeholders to better allocate investment budgets and increase the economic benefits of waterway transportation systems.

Suggested Citation

  • Furkan Oztanriseven & Heather Nachtmann, 2020. "Modeling dynamic behavior of navigable inland waterways," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(2), pages 173-195, June.
    • Handle: RePEc:pal:marecl:v:22:y:2020:i:2:d:10.1057_s41278-019-00127-5
    DOI: 10.1057/s41278-019-00127-5
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    Cited by:

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    2. Istiak Ahmad & Fahad Alqurashi & Ehab Abozinadah & Rashid Mehmood, 2022. "Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation," Sustainability, MDPI, vol. 14(9), pages 1-72, May.

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