• The Journal of Bigdata
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• v.8 no.2
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• pp.165-175
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• 2023

LNG is a representative imported cargo at Incheon Port and has a relatively high contribution to the increase/decrease in overall cargo volume at Incheon Port. In addition, in the view point of nationwide, LNG is the one of the most important key resource to supply the gas and generate electricity. Thus, it is very essential to identify the factors that have impact on the demand fluctuation and build the appropriate forecasting model, which present the basic information to make balance between supply and demand of LNG and establish the plan for power generation. In this study, different to previous research based on macroscopic annual data, the weekly demand of LNG is converted from the cargo volume unloaded by LNG carriers. We have identified the periodicity and correlations among internal and external factors of demand variability. We have identified the input factors for predicting the LNG demand such as seasonality of weekly cargo volume, the peak power demand, and the reserved capacity of power supply. In addition, in order to predict LNG demand, considering the characteristics of the data, time series prediction with weekly LNG cargo volume as a dependent variable and prediction through an artificial neural network model were made, the suitability of the predictions was verified, and the optimal model was established through error comparison between performance and estimates.

• Journal of Korea Port Economic Association
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• v.39 no.1
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• pp.47-63
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• 2023

In smart ports and port automation, the number of vertically deployed container terminals is growing. The purpose of this study is to analyze the productivity of horizontally arranged and vertically arranged container terminals by comparing the main ship operation time, and to recommend future strategies for increasing the operational efficiency of vertically configured container terminals. To achieve our goal, we chose two terminals representating each type, and collected berth allocation status data from 2018 to 2022. Then we analyzed the data using the Accelerated Failure Time (AFT) model, a parametric survival analysis technique. Under the assumption that the working circumstances of the C/C (Container Crane) are the same, we find that the productivity of on-board work of the vertically placed container terminal is higher than that of the horizontally placed container terminal. Our result also shows that the productivity is reduced during the COVID-19 period and the European ships show lower onboard work time. On the basis of these findings, we propose strategies to improve the productivity of vertical container terminals.

• Journal of Navigation and Port Research
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• v.30 no.6 s.112
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• pp.525-531
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• 2006

In the port, the yard crane is very important. If a container crane or a transfer crane is broken down, it costs a lot because of the delay of work during the period of repair or reorder. But, we don't have enough spare parts because of the high cost. It is necessary to maintain high reliability of the crane through effective preventive maintenance and failure analysis. In this paper, we analyse the function and failure mechanism of the transfer crane which is a main equipment in the yard Also, we standardize failures and maintenance works using the historical data of failure and maintenance. This study which is a basic work for effective equipment operation and maintenance will support reliability engineers to decide the optimal design of the next generation equipment and operational policy of equipment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.363-366
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• 2006

In the port, Yard Crane is very important. If container crane or transfer crane broke down, it costs much money for delaying of work during the period of repair or reorder. But, we can not have enough spare parts for its high cost. It is necessary for having the reliability of crane through the effective preventive maintenance and failure analysis. In this paper, we analysed the system's function and failure mechanism of transfer crane which is main equipment in the yard. Also, we standardized the failure and maintenance work using the historical data of failure and maintenance. This study which is the basic work for IT of equipment operation and maintenance is going to be new attempt for optimal design of next generation equipment and operation policy of equipment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.57-58
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• 2023

The working time for loading and transporting containers in the container terminal is one of the factors directly related to port productivity, and minimizing working time for these operations can maximize port productivity. Among working time for container operations, the working time of yard tractors(Y/T) responsible for the transportation of containers between berth and yard is a significant portion. However, it is difficult to estimate the working time of yard tractors quantitatively, although it is possible to estimate it based on the practical experience of terminal operators. Recently, a technology based on IoT(Internet of Things), one of the core technologies of the 4th industrial revolution, is being studied to monitoring and tracking logistics resources within the port in real-time and calculate working time, but it is challenging to commercialize this technology at the actual port site. Therefore, this study aims to develop yard tractor working time prediction model to enhance the operational efficiency of the container terminal. To develop the prediction model, we analyze actual port operation data to identify factors that affect the yard tractor's works and predict its working time accordingly.

• Journal of Korea Port Economic Association
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• v.22 no.4
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• pp.81-98
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• 2006

Many studies on port tariff have been done over twenty years using publicly assessed data on tariff. Public data for tariff rates do not reflect, however, the port tariff in a real market, since the cargo handling charge, which is the important fraction of port tariff, is confidentially decided by the negotiations between a shipping company and a container terminal operator. In this paper, we collected the real price data of the port tariff on the world major sixteen container ports from a global shipping company and transformed it into the tariff per TEU(US$/TEU). The comparative analysis of port tariff was performed using the port tariff per TEU, and a panel regression analysis was done to identify the relations between the port tariff and demand variables: throughput, GDP and trade amount.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.