• International Commerce and Information Review
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• v.16 no.2
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• pp.157-183
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• 2014

The aim of this study is to establish a detailed strategic countermeasure for Korean west coast ports(Pyeongtaek Dangjin Port, Incheon Port, and Gwangyang Port) to be developed into core ports in the Pan-Yellow Sea area as the results such as strategic partnership ports analysis through the container volume analysis in Korean ports are comprehensively taken into account between west coast ports and other major ports in the Pan-Yellow Sea area. This study utilized related data which import and export data by Office of Customs Administration and SPIDC by Ministry of Maritime Affairs and Fisheries for analyzing container volume between two ports. Strategic partnership ports were selected based on in-depth analysis on 5 standards such as container volume in 2012, increase rate of trading, occupancy rate, variance rate, and contribution of container volume. As a result of selection strategic partnership port in Pan-Yellow Sea area, Lianyungang, Tianjin, Yantai, Qingdao, Dalian port in Pyeongtaek Dangjin Port, Shidao, Weihai, Qingdao, Tianjin, Dalian port in Incheon, Qingdao, Yantai, Dalian, Lianyungang port in Gwangyang port. Also this study proposed implications of countermeasure to establish strategic partnership ports for each of west coast ports.

• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.425-431
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• 2008

The future cargo transportation and traffic volume on container in Gwangyang port was forecasted by using univariate time series models in this research. And the container ship traffic was produced. The constructed models all were most adapted to Winters' additive models with a trend and seasonal change. The cargo transportation on container in Gwangyang port was estimated each about 2,756 thousand TEU and 4,470 thousand TEU in 2011 and 2015 by increasing each 7.4%, 16.2% compared with 2007. The volume per ship on container was estimated each about 675TEU and 801TEU in 2011 and 2015 by increasing each 30.3%, 54.6% compared with 2007. Also, traffic volume on container incoming in Gwangyang Port was prospected each about 4,078ships and 5,921ships in 2011 and 2015.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.1-17
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• 2008

In this paper the cargo transportation were forecasted for the North Port in Busan through time series models. The cargo transportation were classified into three large groups; container, oil, general cargo. The seasonal indexes of existing cargo transportation were firstly calculated, and optimum models were chosen among exponential smoothing models and ARIMA models. The monthly cargo transportation were forecasted with applying the seasonal index in annual cargo transportation expected from the models. Thus, the cargo transportation in 2011 and 2015 were forecasted about 22,900 myriad ton and 24,654 myriad ton respectively. It was estimated that container cargo volume would play the role of locomotive in the increase of the future cargo transportation. On the other hand, the oil and general cargo have little influence upon it.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.250-251
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• 2012

지난 15년간 한국경제는 두 번의 금융위기인 1997년의 아시아 금융위기와 2008년의 글로벌 금융위기로 인해 엄청난 경제손실을 입었다. 이와 같이 우리 경제의 고유한 문제이기 보다는 외부에서 발생한 일련의 사건들은 GDP와 같은 국내 거시경제지표들뿐만 아니라 국내 항만들의 컨테이너처리물동량에도 많은 영향을 주었다. 본 연구에서 두 번의 금융위기를 독립적이며 상이한 형태의 외부의 영향으로 가정한 뒤, 국내 항만들의 컨테이너처리물동량에 어떤 영향을 주었는지에 대한 개입분석을 수행한다. 그래서 각기 다른 충격들에 대한 컨테이너처리량을 변화와 함께 그 충격들 간의 특성규명을 통해 우리나라 항만산업에 다양한 시사점을 제공하고자 한다.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.179-196
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• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• Journal of Korea Port Economic Association
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• v.23 no.2
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• pp.1-24
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• 2007

With its foreign trade rapidly expanding and with economic growth continuing at a substantial rate, China has become the largest container traffic generating country in the world. And with trend of container ships becoming larger and faster, the environment surrounding ports in North-East Asia are rapidly changing. The Yangsan, offshore port for Shanghai, being developed on the islands of Da Yangsan and Xiao Yangsan, some 30km offshore, and connected to the mainland by the six-lane highway Donghai bridge, opened phase one in late 2005 and phase two in 2006 respectively and will have 29 berths by 2012 and be able to handle 15 million TEU. The Port of Shanghai which passed Busan in terms of container volume further consolidated its position as the world's No. 3 port with an annual volume of 21.7 million TEU in 2006 and is likely to have emerged as the biggest container port in the world. The Port of Busan, the world's fifth largest container port, wants to survive as regional hub port. In this circumstance, the strategies of the Port of Busan should be established.

• Journal of Korea Port Economic Association
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• v.26 no.2
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• pp.139-149
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• 2010

This paper analyses the relative efficiency of 13 container terminals based on the data for the period 2003-8 to offer a fresh perspective. There has been abundant empirical research undertaken on the technical efficiency of Busan and Gwangyang port. Most studies have focused on the use of parametric and non-parametric techniques to analyse overall technical efficiency. Here, the framework assumes that terminals use two input to produce one output; the former includes container yard and container crane and the latter container volume. Jarque-Bera indicates that three variables are not normally distributed and the positive skewness shows that all the variables have long right tails. This means there are many small-scaled container terminals. This paper also employs heteroscedastic Tobit model to show the effect of the explanatory variables on the container terminal efficiencies. The Tobit model shows that both container yard and container cranes have positive effect on the container terminal efficiency, but container yard has a higher impact on the efficiency than the container crane.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.25-39
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• 2023

As the scope of supply chains expands globally, unpredictable risks continue to arise. The occurrence of these supply chain risks affects port cargo throughput and hinders port operation. In order to examine the impact of global supply chain risks on port container throughput, this study conducted an empirical analysis on the impact of variables such as the Global Supply Chain Pressure Index (GSCPI), Shanghai Container Freight Index (SCFI), Industrial Production Index, and Retail Sales Index on port traffic using the vector autoregressive(VAR) model. As a result of the analysis, the rise in GSCPI causes a short-term decrease in the throughput of Busan Port, but after a certain point, it acts as a factor increasing the throughput and affects it in the form of a wave. In addition, the industrial production index and the retail sales index were found to have no statistically significant effect on the throughput of Busan Port. In the case of SCFI, the effect was almost similar to that of GSCPI. The results of this study reveal how risks affect port cargo throughput in a situation where supply chain risks are gradually increasing, providing many implications for establishing port operation policies for future supply chain risks.

• 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.

• Journal of Korea Port Economic Association
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• v.34 no.2
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• pp.97-114
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• 2018

The East Asian region, an important part of Korea's imports and exports, is expected to grow further driven by the geographical, political, economic, social, and cultural complementarity. With the recent increase in imports and exports, the port trade volume between Korea and East Asian countries is also growing. However, due to various factors, such as economic size, growth rate, port infrastructure level, and geographical location of these countries, the volume of traffic with these ports is fluctuating. Despite much research on the volatility of port trade volume and changes in port network, this study tries to supplement the gap in a more detailed study of ports in Korea and East Asia since these kinds of studies are limited. The purpose of this study is to analyze the trend of distribution routes of export container cargo among ports in Korea and to present policy and practical implications of Korean trading companies, shipping companies, logistics companies, and port authorities. This study analyzes the variability of the trade volume between Korea's major ports and Daedong. Results show that Shanghai, Ningbo, Ho Chi Minh, and Haiphong were the most important factors in terms of size and volume increase. In terms of ports, the Busan port is the port responsible for trades with Yantai, Weihai, Hakata, Kobe, Ho Chi Minh, and Haiphong; Incheon port deals with Lianyungang, Tianjin, Osaka, Kobe, Ho Chi Minh, Haiphong; Gwangyang port trades with Tianjinxingang, Weihai, Yokohama, Mihn and Tanjong, and Ulsan port is strategically important for the Yantai, Lianyungang, Nagoya, Kobe, Ho Chi Minh and Portkelang ports. Therefore, the Korean government, port authorities, and shipping and logistics companies need to strengthen logistic network cooperation with these ports and actively promote investments in them.