Date of Graduation

8-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Industrial Engineering

Advisor/Mentor

Nachtmann, Heather L.

Committee Member

Parnell, Gregory S.

Second Committee Member

Liu, Xiao

Third Committee Member

Hernandez, Sarah V.

Keywords

Barge shipping; Container shipping; Feasibility assessment; Inland waterway; Intermodal transportation; Volume forecasting

Abstract

Container on Barge (COB) transportation is an intermodal freight transport mode that moves shipping containers via barges on navigable inland and intracoastal waterways. During the past twenty years, COB has been a growing mode of container shipping globally due to its low-cost, eco-friendly, and congestion-reducing characteristics. Europe and China are currently leading global COB transportation, and the United States (U.S.) may have the potential to achieve economic benefits through the implementation of COB within its intermodal transportation system. To explore this potential, this dissertation investigates the implementation feasibility of COB transportation within the U.S. intermodal freight transportation system. Three contributions are included in this dissertation: 1) a literature review to systematically describe the development and status of COB transportation research, 2) a Value-Focused Thinking-based decision model to assess the feasibility of implementing COB at inland waterway ports within the U.S., and 3) a machine learning study to perform COB volume forecasting by utilizing economic indicators. This dissertation research assists maritime transportation decision-makers and individual inland waterway port/terminal operators to: 1) adopt success practices from global COB development, 2) comprehensively and practically assess the feasibility of COB development based on values identified from successful implementation, 3) develop a method to accurately forecast COB volume on inland waterways by using machine learning methods and economic data, and 4) lay a foundation to build transfer learning models to forecast COB volume for U.S. inland waterway.

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