• 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.46 no.4
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• pp.344-350
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• 2022

Due to the increase in container cargo volume, the congestion of container terminals is increasing and the waiting time of gate in-out trucks has significantly lengthened at container yards and gates, resulting in severe inefficiency in gate in-out truck operations as well as port operations. To resolve this problem, the Busan Port Authority and terminal operator provide services such VBS, terminal congestion information, and expected operation processing time information. However, the visible effect remains insufficient, as it may differ from actual waiting time.. Thus, as basic data to resolve this problem, this study presents deep learning based average gate in-out truck waiting time prediction models, using container gate in-out information at Busan New Port. As a result of verifying the predictive rate through comparison with the actual average waiting time, it was confirmed that the proposed predictive models showed high predictive rate.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.1-10
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• 2004

Video-On-Demand servers use compression techniques to reduce the storage and bandwidth requirements. The compression techniques make the bit rates of compressed video data significantly variable from frame to frame. Consequently, Video-On-Demand servers with a constant bit rate retrieval can not maximize the utilization of resources. It is possible that when variable bit rate video data is stored, accurate description of the bit rate changes could be computed a priori. In this paper, I propose a buffer management scheme called MAX for Video-On-Demand server using variable bit rate continuous media. By caching and prefetching the data, MAX buffer management scheme reduces the variation of the compressed data and increases the number of clients simultaneously served and maximizes the utilization of system resources. Results of trace-driven simulations show the effectiveness of the scheme.