• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.5
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• pp.483-490
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• 2013

In this study, we analyzed AIS measured data for ten days by selecting the four main ports with many ships arriving in the national ports. The peak time congestion of the main ports, calculated by survey research, was about 3.8-5.7 times higher than the hourly average congestion. This is very different from the results of the advanced research, evaluating the marine traffic congestion of the Ulsan main port based on the existing Port-MIS statistical data, which showed a peak time congestion of about 1.7 times higher than the hourly average. This identifies the problem of distorting the traffic characteristics of the current passage. Therefore, in order to evaluate marine traffic congestion, it would be more appropriate to calculate it based on survey research, rather than Port-MIS statistical data.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.1
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• pp.1-8
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• 2017

In this study, a congestion assessment was conducted to verify seasonal differences in congestion for major coastal traffic routes and fairways in major ports with GICOMS Data for 7 days without issuing a special weather report. As a result, a maximum of 11 % and 82 % are shown, with an average of 3.5 % and a 30 % seasonal difference for hourly average congestion and peak time congestion. Therefore, seasonal differences for the target area should be taken into consideration to perform further congestion assessments, particularly for maritime traffic safety assessments, and keen attention should be given to setting up safety measures against congestion.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.4
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• pp.381-388
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• 2018

This study proposes marine traffic volume estimation method considering ship speed, a factor excluded from the existing method. Ten days of GICOMS marine traffic data from Pyeongtaek and Dangjin ports was applied to this study. As a result, converted traffic volume with the proposed estimation method showed an increase of 4.41 (${\pm}0.99$) times or decrease of 0.59 (${\pm}0.04$) at most, compared with the existing estimation method. Average marine traffic congestion for each time applying the proposed estimation method showed an increase of 1.43 (${\pm}0.10$) compared with the existing estimation method. The maximum marine traffic congestion for each time was 1.62 (${\pm}0.34$) times higher compared with the existing estimation method. Marine traffic peak time, defined as the highest point of marine traffic congestion, was evaluated to be different from that of the existing method because of distribution of vessel speed. In conclusion, considering ship speed is necessary when estimating marine traffic volume to produce a practical estimate of marine traffic capacity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.669-674
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• 2017

Due to the depletion of fossil fuels and the increase in environmental pollution, electric vehicles are attracting attention as next-generation transportation and are becoming popular all over the world. As the interest in electric vehicles and the penetration rate increase, studies on the charging infrastructure with vehicle-to-grid (V2G) technology and information technology are actively under way. In particular, communication with the grid network is the most important factor for stable charging and load management of electric vehicles. However, with the existing centralized infrastructure, there are problems when control-message requests increase and the charging infrastructure cannot efficiently operate due to slow response speed. In this paper, we propose a new charging infrastructure using mobile edge computing (MEC) that mitigates congestion and provides low latency by applying distributed cloud computing technology to wireless base stations. Through a performance evaluation, we confirm that the proposed charging infrastructure (with low latency) can cope with peak conditions more efficiently than the existing charging infrastructure.

• Journal of Korea Port Economic Association
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• v.34 no.2
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• pp.69-82
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• 2018

The number of ships serviced at the container terminals in Busan is increasing by 2.9% per year. In spite of the increase in calling ships, there are no official records of waiting rate by the port authority. This study attempts to compare the theoretical ship waiting ratio and actual ship waiting ratio. The actual ship waiting ratio of container terminals is acquired from the 2014 to 2016 data of PORT-MIS and Terminal Operating System (TOS). Furthermore, methods and procedures to measure the actual ship's waiting rate of container terminal are proposed for ongoing measurement. In drawing the theoretical ship waiting ratio, the queuing theory is applied after deploying the ship arrival probability distribution and ship service probability distribution by the Chi Square method. As a result, the total number of ships waiting in a terminal for three years was 587, the average monthly service time and the average waiting time was 13.8 hours and 17.1 hours, respectively, and the monthly number of waiting ships was 16.3. Meanwhile, according to the queuing theory with multi servers, the ship waiting ratio is 31.1% on a 70% berth occupancy ratio. The reason behind the huge gap is the congested sailing in the peak days of the week, such as Sunday, Tuesday, and Wednesday. In addition, the number of waiting ships recorded on Sundays was twice as much as the average number of waiting ships.