Network assignment model of integrating maritime and hinterland container shipping: application to Central America

Ryuichi Shibasaki (), Takayuki Iijima, Taiji Kawakami, Takashi Kadono and Tatsuyuki Shishido
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Ryuichi Shibasaki: National Institute for Land and Infrastructure Management, Ministry of Land, Infrastructure, Transport and Tourism (MLIT)
Takayuki Iijima: Port and Harbor Bureau, Tokyo Metropolitan Government
Taiji Kawakami: MLIT
Takashi Kadono: MLIT
Tatsuyuki Shishido: The Overseas Coastal Area Development Institute of Japan (OCDI)

Maritime Economics & Logistics, 2017, vol. 19, issue 2, No 4, 234-273

Abstract: Abstract The authors develop a model to predict worldwide container movements including both maritime and land shipping network from the viewpoint of cargo owners, given the liner shipping network provided by shipping companies and the level of service in each port. The network assignment methodology is applied to both an intermodal shipping network and maritime shipping sub-network, by which the solution can be obtained in a huge, real-scale network including more than 150 worlds’ container ports as well as some hinterland network of the world. The developed model is applied to the Central American region, where the international maritime containers are often transported across national borders by land. It is confirmed that the model output agrees with the actual container movement in terms of the container cargo throughput for each port, land container flow, and maritime flow by shipping company in Central America. Also, the model sensitivity to key parameters included in the model is confirmed reasonable. Finally, it is also confirmed that the model can predict the volume of containers handled in the port of La Union, where no liner service had previously called and a new liner service calls.

Keywords: international container cargo; intermodal shipping network; stochastic network assignment model; generalized shipping cost; ocean freight charge; Central America (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1057/s41278-016-0055-3

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