In this research, we developed a novel model framework consisting of data mining (DM), linear programming (LP), and an all-or-nothing (AON) flow assignment to estimate maritime freight flows between the United States and the rest of the world. We first built DM and LP models to select and combine various country-level data sources on import and export freight into a complete geographic information system (GIS)-based origin and destination (OD) database with targeted locations, networks, and attributes on ocean routes connecting foreign and U.S. maritime ports. Then, we performed freight assignments and estimated total or commodity-specific import and export freight flows. Additionally, we visualized major sea ports with various handling capacities and optimal maritime freight flows in 2D in GIS and in 3D in Google Earth with highlights for selected total and most imported or exported goods on maritime networks and for major trading partners, such as the U.S. and China. Finally, a visual validation of model results on optimal maritime routes with respect to real-time vessel density network links and routes was provided as well.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited