• Journal of Korean Port Research
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• v.12 no.2
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• pp.177-194
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• 1998

In Pusan port, the studies which analyze container cargo volumes by using forecasting methods and research about container logistics system, etc., have been continuously carried out. But, in Pusan port, the study on an evaluation of traffic congestion has been scarcely performed until now. Especially, when changing and extending a berth, or constructing a new port, it is very important to examine this field. And it should be considered. Thus, this paper aims to analyze the effect of ship traffic condition in the year 2011, to evaluate marine traffic congestion according to changing ship traffic volumes in Pusan port. To analyze it, we examined the results by simulation method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.155-161
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• 2023

97.5% of our country's exports and 87.2% of imports are transported by sea, making ports an important component of the Korean economy. To efficiently operate these ports, it is necessary to improve the short-term prediction of port water volume through scientific research methods. Previous research has mainly focused on long-term prediction for large-scale infrastructure investment and has largely concentrated on container port water volume. In this study, short-term predictions for petroleum and liquefied gas cargo water volume were performed for Ulsan Port, one of the representative petroleum ports in Korea, and the prediction performance was confirmed using the deep learning model LSTM (Long Short Term Memory). The results of this study are expected to provide evidence for improving the efficiency of port operations by increasing the accuracy of demand predictions for petroleum and liquefied gas cargo water volume. Additionally, the possibility of using LSTM for predicting not only container port water volume but also petroleum and liquefied gas cargo water volume was confirmed, and it is expected to be applicable to future generalized studies through further research.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.295-303
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• 2017

In this paper, we analyze the trends of the international shipping market and the domestic and foreign factors of the crisis of the domestic shipping market, and identify the characteristics of the recovery of the Busan New Port trade volume which has decreased since the crisis of the domestic shipping market We quantitatively analyzed the future volume of Busan New Port and analyzed the trends of the prediction and recovery trends. As a result of analyzing Busan New Port container cargo volume by using big data analysis tool R, the variation of Busan New Cargo container cargo volume was estimated by ARIMA model (1,0,1) (1,0,1)[12] Estimation error, AICc and BIC were the most optimal ARIMA models. Therefore, we estimated the estimated value of Busan New Port trade for 36 months by using ARIMA (1, 0, 1)[12], which is the optimal model of Busan New Port trade, and estimated 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, and 4,526,406 TEU, respectively, indicating that it increased by about 2%, 2%, and 1%.

• 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 Korea Port Economic Association
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• v.25 no.4
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• pp.131-146
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• 2009

The purpose of this study is to predict the railway cargo volume on Busan new-port and north-port, in order to revitalize railway transport. This paper is organized as follows. Section 1 presents the description of the objective and methods on this study. Section 2 presents the status of Railway Cargo volumes and Construction plan of railway facilities in Busan New port. Section 3 presents the Forecast Railway Cargo volume using a volume ratio, actual volume records and another predicted datas. Section 4 summarizes our conclusions and further research topics. Especially, korea faces enforcement of green Logistics policy. Modal shift to trail freight transportation is one of ways, but there are no more detail plans. so it need that a cooperation system in government department, a indirect subside policy shift to rail freight transportation from trucking for revitalization of Railway Freight transportation.

• Korea Trade Review
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• v.43 no.5
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• pp.159-177
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• 2018

The container shipping sector is an important international logistics operation that connects open economies. Freight rates rapidly change as the market fluctuates, and staff related to the shipping market are interested in factors that determine freight rates in the container market. This study uses the Vector Error Correction Model(VECM) to estimate the impact of factors affecting container freight rates. This study uses data published by Clarksons. The analysis results show a 4.2% increase in freight rates when world container traffic increases at 1.0%, a 4.0% decrease in freight rates when volume of container carriers increases by 1.0%, a 0.07% increase in freight rates when bunker price increases by 1.0%, and a 0.04% increase in freight rates accompanying 1.0% increase in libor interests rates. In addition, if the current freight rate is 1.0% higher than the long-term equilibrium rate, the freight rate will be reduced by 3.2% in the subsequent term. In addition, if the current freight rate is 1.0% lower than the long-term equilibrium rate, the freight rate will decrease by 0.12% in the following term. However, the adjusting power in a period of recession is not statistically significant which means that the pressure of freight rate increase in this case is neglectable. This research is expected to contribute to the utilization of scientific methods in forecasting container freight rates.

• Journal of Navigation and Port Research
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• v.29 no.9
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• pp.795-800
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• 2005

Nowadays, road transportation which has played a key role in the market of both passenger and freight transportation is facing with a serious problem, the traffic congestion causing a delay of transportation. Therefore, railroad transportation is considered as an attractive alternative mode of inland transportation due to its inherent merits in mass transportation such as relatively low cost compared with road transportations, less air pollution and noise than other mode ets. In this paper, therefore, we examine the current situation of railroad transportation markets including TKR(Trans- Korean Railway}, TSR(Trans-Siberian Railway} and prospects for the connection of TKR-TSR. And then we examine the structure of the container transportation market by railroad in Korea with a brief analysis of the traffic volume of TKR-TSR.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.