• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.83-104
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• 2010

The purpose of this paper is to prepare container port policy and to contribute to the regional economy by forecasting of the container freight volume for the Donghae Port and Sokcho Port. As a methodology a survey and O/D technique were adopted. O/D technique was applied to the container freight data of Korea Maritime Institute. The main results of this paper are as follows: First, it is adviserable that Gangwondo Province should adopt incentive program of 100,000 won Per TEU rather than 50,000 won per TEU. Secondly, container freight volume for Donghae Port and Sokcho Port is forecast to be 22,388 TEU in 2010, 152,367 TEU in 2015 and 354,217 TEU from 6,653 TEU in 2008. Thirdly, joint port marketing is required for the Donghae Port and Sokcho Port in terms of same region in one hour drive.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.600-614
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• 2014

This paper develops a model to forecast container volumes of all Korean seaports using a Seasonal ARIMA (Autoregressive Integrated Moving Average) technique with the quarterly data from the year of 1994 to 2010. In order to verify forecasting accuracy of the SARIMA model, this paper compares the predicted volumes resulted from the SARIMA model with the actual volumes. Also, the forecasted volumes of the SARIMA model is compared to those of an ARIMA model to demonstrate the superiority as a forecasting model. The results showed the SARIMA Model has a high level of forecasting accuracy and is superior to the ARIMA model in terms of estimation accuracy. Most of the previous research regarding the container-volume forecasting of seaports have been focussed on long-term forecasting with mainly monthly and yearly volume data. Therefore, this paper suggests a new methodology that forecasts shot-term demand with quarterly container volumes and demonstrates the superiority of the SARIMA model as a forecasting methodology.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.157-163
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• 2017

Forecasting container cargo volumes is very important factor for port related organizations in inversting in the recent port management. Especially forcasting of domestic and foreign container volume is necessary because adjacent nations are competing each other to handle more container cargoes. Exact forecasting is essential elements for national port policy, however there is still some difficulty in developing the predictive model. In this respect, the purpose of this study is to develop and suggest the forecasting model of container cargo volumes of China using System Dynamics (SD). The monthly data collected from Clarkson's Shipping Intelligence Network from year 2004 to 2015 during 12 years are used in the model. The accuracy of the model was tested by comparisons between actual container cargo volumes and forecasted corgo volumes suggested by the research model. The MAPE values are calcualted as 6.21% for imported cargo volumes and 7.68% for exported cargo volumes respectively. Less than 10% of MAPE value means that the suggested model is very accurate.

• Journal of Navigation and Port Research
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• v.31 no.5 s.121
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• pp.353-359
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• 2007

Effective plan and operation managements can be established in advance if the traffic volume of container ship will be forecasted in the trend for container port's cargo volume to increase. At the viewpoint for marine traffic the number of incoming and outgoing container ship can be presumed in the long run and organised rational plan to deal the demand of marine traffic on the basis. Therefore, the paper estimated the future traffic volume of incoming and outgoing container ship for Busan, Gwangyang, and Incheon port on a forecasting data basis of container volume suggested in the national ports base plan. The trends of volume per ship on container were estimated with ARIMA models and seasonal index was computed. Thus the traffic volume of container ship in the future was estimated computing with volume per ship in 2011,2015, and 2020 respectively.

• Journal of Korean Society of Transportation
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• v.19 no.2
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• pp.29-40
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• 2001

부산항은 우라나라의 전체 수출입 및 환적 컨테이너 물동량의 약 90%를 처리하고 있기 때문에 지금까지 꾸준한 물동량 증가 추세를 보이고 있다. 컨테이너 물동량의 증가에 따라 정부에서는 항만시설을 지속적으로 확충하여 컨테이너 처리능력을 확대하고 있으나, 컨테이너 물동량의 증가율이 컨테이너 처리시설 확보율을 초과하여 부산항 컨테이너 전용부두의 컨테이너 수용능력은 부족한 실정이다. 이와 같은 컨테이너 장치장 부족문제를 해결하기 위해서 그 동안 부산항의 ODCY에서 처리하였으나, 최근 부두밖 장치장의 단계적 이전 및 폐쇄방침이 결정됨에 따라 부산항의 장치장 부족문제가 대두되고 있는 실정이다. 따라서 본 연구에서는 장치장 부족문제를 해결하고 부산항 컨테이너 유통체제를 개선시킬 수 있는 방안을 제시하는데 목적을 두고 있다. 이를 위하여 첫째, 부산항의 컨테이너화물 유통 현황 및 문제점을 분석하고 둘째, 부산항 컨테이너화물 유통체제의 개선대안을 설정하여 분석결과를 제시한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.166-169
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• 2011

항만간 허브항 경쟁이 극심해 지고 있는 오늘날, 컨테이너 선사는 M&A 및 전략적 제휴로 컨테이너터미널 운영사와의 가격 협상력의 우월적 지위를 갖게 되어 컨테이너터미널 운영사간 선사 및 화물유치를 경쟁을 더욱 부추기고 있다. 그러나 수요측면에서 컨테이너물동량 증가율 둔화로 컨테이너터미널에서 처리해야 할 물동량은 한정되어 있는 반면, 공급 측면에서 항만터미널의 지속적인 건설은 항만간 또는 터미널간 물량 유치경쟁을 과열시키고 있다. 특히 부산항은 신항 개장이후 북항과 신항간 물동량 유치경쟁으로 인하여 항만하역시장의 교란을 가져오고 있다. 본 연구에서는 부산항 컨테이너 항만하역시장의 구조적 특성분석과 설문조사 방법론을 통하여 향후 부산항 항만하역시장의 안정화 방안을 제시하고자 한다. 시장구조 분석결과 부산항은 한정된 처리물량과 신항의 개장으로 인한 공급과잉, 그리고 정부의 지역항만개발정책에 따른 컨테이너화물의 분산처리로 지속적인 부산항의 비중 감소로 선석당 처리물량이 감소하고 있다. 이에 따라 선사의 우월적 지위를 이용한 하역료 인하요구로 터미널운영사간에 서비스경쟁이 아닌 비협력적인 가격경쟁으로 재정수지가 악화되고 있고, 또한 '10년 외국적선사의 처리물량이 60%를 차지하고 있어 국부유출이 심각한 실정이다. 따라서 하역시장 안정화 방안으로 항만시설 수요 및 공급의 불균형을 조정하기 위하여 항만풀링공동기금관리를 통한 재정수지를 확보할 수 있는 항만풀링제도를 제안하며, 이 제도의 운영을 위하여 한시적으로 컨테이너터미널 운영사별 처리물량 상한제를 도입하는 것이 바람직하다고 판단된다.

• Journal of Korea Port Economic Association
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• v.32 no.1
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• pp.97-122
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• 2016

Incheon port attained two million TEU of container throughput between 2013 and 2014 as a third port in domestic container throughput. It opened a new port in Song-do, Incheon in June 2015 to prepare for the continuing increase in container throughput.Therefore, it has provided the platform for being the major container port domestically and internationally. As the role of the new port increases, the role and direction of the Incheon port liner service network attracts attention. This study analyzes the centrality of the Incheon port liner service network by using SNA (Social Network Analysis), which was introduced in the maritime economics area recently, focusing on the Incheon port liner service network. We recognize the degree centrality, closeness centrality, and betweenness centrality of each port and its effect on the Incheon port liner service network. The study showed that for Incheon port, the centrality of the Busan port in Korea, and the Hong Kong port, is high outside the country. This helps us determine that the hub of the Incheon port is neither Shanghai nor Singapore, which ranks first and second, respectively, on container throughput. It is also helps us to know that eastern China's ports have not played a role of the hub of the Incheon port until now because of the relatively low centrality of eastern China's ports.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.53-62
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• 2020

The maritime and port logistics industry is closely related to global trade and economic activity, especially for Korea, which is highly dependent on trade. As the largest port in Korea, Busan Port processes 75% of the country's container cargo; the port is therefore extremely important in terms of the country's national competitiveness. Port container cargo volume forecasts influence port development and operation strategies, and therefore require a high level of accuracy. However, due to unexpected and sudden changes in the port and maritime transportation industry, it is difficult to increase the accuracy of container volume forecasting using existing time series models. Among deep learning models, this study uses the LSTM model to enhance the accuracy of container cargo volume forecasting for Busan Port. To evaluate the model's performance, the forecasting accuracies of the SARIMA and LSTM models are compared. The findings reveal that the forecasting accuracy of the LSTM model is higher than that of the SARIMA model, confirming that the forecasted figures fully reflect the actual measurement figures.

• Journal of Korea Port Economic Association
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• v.32 no.4
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• pp.165-182
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• 2016

This study examines the competitiveness and cooperativeness among the container ports in East Asia by analyzing their monthly dynamics in eight years (2008-2015). Time series data on container throughput divided into origin and destination (O/D), such as the top six Chinese ports and the transshipment (T/S) ports such as Hong Kong, Busan, and Singapore, are computed with two methods based on the Vector Error Correction Model (VECM). The first Granger causality test results show that Busan T/S has significant bilateral relations with three Chinese O/D ports; and significant unidirectional relations with three other O/D ports. Shenzhen port has significant bilateral relations with Singapore, and has a significant unidirectional relation with Hong Kong port. Co-integrating test results showed that Busan holds negative co-integration with all Chinese O/D ports. Impulse response function (IRF) results show an opposite direction between paired ports. The ratios of the impulse from T/S ports are significantly high to one another in the short-run, but its power declines as time passes. The ratio of the impulse from the Chinese ports to T/S ports is less significant in the short-run period, however, it becomes more significant as time passes. The significance of most shocks was high in the second period, but was diluted after the sixth period.