• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.165-166
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• 2015

Due to the international economic downturn and the increasing competition, container terminals have been trying to achieve cost effectiveness and high service levels. Shipping companies want to get better services from container terminals but container terminals only have limited space and equipment. Frequently, too many external trucks come to the terminal at the same time, and create bottle necks at the terminals. Ouside the container terminals traffic jams occur as well. As a result, stakeholders need a vehicle booking system. By VBS, container terminals can plan efficiently in advance to avoid work delay and the truck drivers can finish their jobs quickly. This paper aims to search efficient models for the container terminals by using VBS.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.27-33
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• 2024

Following the COVID-19 pandemic, congestion within container terminals has led to a significant increase in waiting time and turnaround time for external trucks, resulting in a severe inefficiency in gate-in and gate-out operations. In response, port authorities have implemented a Vehicle Booking System (VBS) for external trucks. It is currently in a pilot operation. However, due to issues such as information sharing among stakeholders and lukewarm participation from container transport entities, its improvement effects are not pronounced. Therefore, this study proposed a deep learning-based predictive model for external trucks turnaround time as a foundational dataset for addressing problems of waiting time for external trucks' turnaround time. We experimented with the presented predictive model using actual operational data from a container terminal, verifying its predictive accuracy by comparing it with real data. Results confirmed that the proposed predictive model exhibited a high level of accuracy in its predictions.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.371-374
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• 1998

The construction of the transportation history database system is to serve the scheduling and seat inventory controling. Recently, lots of countries have been faced with the advance era because of the new railway transportation system, like the high speed railway and/or magnetic levitation vehicle system. This can be reasonably translated as those of operators are willing to provide the more various and high quality schedule to the customer. Those operators these ideas make possible to forecast that scheduling process is going to be complicated more and more. The seat inventory control, so to speak Yield Management System(YMS), goes a long way to improve the total passenger revenue at the railway business. The YMS forecasts the number of the last reservation value(DCP# END) and recommends the optimal values on the seat sales. The history database system contains infra-data(ie, train, seat, sales) that will be the foundation of scheduling and seat inventory control application programs. The development of the application programs are reserved to the next step. The database system is installed on the pc platform (IBM compatible), using the DB2(RDBMS). And at next step, the platform and DBMS will be considered whether they can meet the users' requirement or not.