• 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 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 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.

• 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 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 Korea Port Economic Association
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• v.23 no.1
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• pp.1-18
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• 2007

The purpose of this study is to examine the effect of exchange rate volatility on Trading Volume of Container of Korea, and to induce policy implication in the contex of GARCH and regression model. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply impulse response functions and variance decomposition to the structural model to estimate dynamic short run behavior of variables. The major empirical results of the study show that the increase in exchange rate volatility exerts a significant negative effect on Trading Volume of Container in long run. The results Granger causality based on an error correction model indicate that uni-directional causality between trading volume of container and exchange rate volatility is detected. This study applies impulse response function and variance decompositions to get additional information regarding the Trading Volume of Container to shocks in exchange rate volatility. The results indicate that the impact of exchange rate volatility on Trading Volume of Container is negative and converges on a stable negative equilibrium in short-run. Th exchange rate volatility have a large impact on variance of Trading Volume of Container, the effect of exchange rate volatility is small in very short run but become larger with time. We can infer policy suggestion as follows; we must make a stable policy of exchange rate to get more Trading Volume of Container

• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.67-81
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• 2016

This paper examines the influence of Brexit on container volume of Korea, especially of macroeconomic variables such as exchange rate and industrial production of EU and United Kingdom. To do this, we use monthly time series data during 2000-2016, and introduce the analysis method of cointegration test and VECM, and analyze the influence of industrial production and exchange rate of EU and U.K. on container volume of Korea. The results are as follows. First, the container volume of Korea is influenced by the exchange rate and industrial production of EU in the long run. But the exchange and industrial production of U.K. influenced on only export container volume of Korea, and the influence of U.K. macroeconomic variables on container volume of Korea was not large in the long lun. Second, In the shot run, the influence of exchange rate on container volume of Korea, especially on export container volume was significant in EU and U.K. To sum up, the influence of EU macroeconomic variables on container volume of Korea is larger than that of U.K., and the influence of exchange rate variable is more significant than that of industrial production variable.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.1-23
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• 2013

This paper estimates and forecasts the container throughput of Busan port using the monthly data for years 1992-2011. To do this, this paper uses the several seasonal multiplicative ARIMA models. Among several ARIMA models, the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$ is selected as the best model by AIC, SC and Hannan-Quin information criteria. According to the forecasting values of the selected seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$, the container throughput of Busan port for 2013-2020 will increase steadily annually, but there will be some volatile variations monthly due to the seasonality and other factors. Thus, to forecast the future container throughput of Busan port and to develop the Busan port efficiently, we need to use and analyze the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$.

• 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 Navigation and Port Research
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• v.26 no.2
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• pp.183-188
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• 2002

The forecast of a container traffic has been very important for port and development. Generally, Statistic methods, such as moving average method, exponential smoothing, and regression analysis have been much used for traffic forecasting. But, considering various factors related to the port affect the forecasting of container volume, neural network of parallel processing system can be effective to forecast container volume based on various factors. This study discusses the forecasting of volume by using the neural, network with back propagation learning algorithm. Affected factors are selected based on impact vector on neural network, and these selected factors are used to forecast container volume. The proposed the forecasting algorithm using neural network was compared to the statistic methods.