A Spatial-Economic Multimodal Transportation Simulation Model For US Coastal Container Ports
M Luo () and
T A Grigalunas ()
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M Luo: Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA.
T A Grigalunas: Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, RI 02881, USA.
Maritime Economics & Logistics, 2003, vol. 5, issue 2, 158-178
Assessing the potential demand for container ports and related multimodal transportation is critical for several purposes, including financial feasibility analysis and the evaluation of net economic benefits and their distribution. When developed in conjunction with a geographical information system, port-related demand analysis also provides needed input for assessment of selected environmental issues, such as truck traffic on local roads and related potential external costs, such as air pollution and noise. However, container port demand analysis is very difficult due to the complexities of international trade in containerised goods, inter-port competition, and potential strategic behaviour by several parties. Difficulties also arise from the many factors to be considered, major data requirements, and the computationally intensive nature of the problem. This paper summarises the development and application of a spatial-economic, multimodal container transportation demand simulation model for major US container ports. The underlying economic framework assumes shippers minimise the total general cost of moving containers from sources to markets. The model is validated and then used to estimate (1) annual container transportation service demand for major container ports, (2) the market areas served by selected ports, and (3) the impact on port demand and interport competition due to hypothetical changes in port use fees at selected ports. This paper first describes the model and the underlying economic reasoning, followed by the assumptions, computational algorithms, and the software architecture. Then, the trade data, transportation networks, and economic variables are described. After that, model simulation results are presented with qualifications, needed refinements, and future directions. Maritime Economics & Logistics (2003) 5, 158–178. doi:10.1057/palgrave.mel.9100067
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