• 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.

• Journal of Navigation and Port Research
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• v.35 no.4
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• pp.343-349
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• 2011

The forecasting of container volume which is the basis of port logistics facilities expansion has a great influence on development of an port. Based on this importance, various previous studies have presented methodology on container volume forecasting. The results of many previous studies pointed out the limitations of future forecasting based on past container volume and emphasized that more various factors should be considered to compensate this. Taking notice of this point, this study forecasted future container volume by using ARIMA model, time series analysis and System Dynamics (SD) method, a dynamic analysis technique and performed the comparative review with the forecast of the Ministry of Land, Transport and Maritime affairs. Recently with rapid changes in economic and social environment, the non-linear change tendency for forecasting container traffic is presented as a new alternative to the country.

• Korea Trade Review
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• v.45 no.5
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• pp.75-89
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• 2020

The purpose of this paper was to analyze the trend of container volume using the Seasonal Autoregressive Intergrated Moving Average (SARIMA) model. To this end, this paper used monthly time-series data of the East Sea Rim from 2001 to 2019. As a result, the SARIMA(2,1,1)12 model was identified as the most suitable model, and the superiority of the SARIMA model was demonstrated by comparative analysis with the ARIMA model. In addition, to confirmed forecasting accuracy of SARIMA model, this paper compares the volume of predict container to the actual volume. According to the forecast for 24 months from 2020 to 2021, the volume of containaer increased from 60,100,000Ton in 2020 to 64,900,000Ton in 2021

• 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 Institute of Industrial Engineers
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• v.37 no.4
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• pp.297-303
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• 2011

Major ports in Northeastern Asia engage in fierce competition to attract transshipment traffic volume. Existing time series analyses for analyzing port competition relationships examine the types of competition and relations through the signs of coefficients in cointegration equations using the transshipment traffic volume results. However, there are cases for which analyzing competing relationships is not possible based on the results of the transshipment traffic volume data differences and limitations in the forecasting of traffic volume. Accordingly, we used the Lotka-Volterra (L-V) model,also known as the ecosystem competitive relation model, to analyze port competition relations for the long-term forecast of South Korean transshipment traffic volume.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.49-58
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• 2014

Variability factors of transship cargo in the container transportation market analysis short term factors. In the past, studies on the factor of variability in container cargo volume have focused on long term volume forecast and increase in investment and competitiveness from strategic perspectives. Unlike previous studies, this paper analyzes factors of variability in transshipment volume rapidly varying in short term and seeks measures. Since it was identified that transshipment volume depends on vessel operation cost and port volume in long term but effectively on special strategies launched by port authorities in short term, the port authority experienced rapid drop in volume should continue to observe strategies of competition ports and to make use of strategies seeking appropriate countermeasures.

• 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 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.

• The Journal of Bigdata
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• v.6 no.2
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• pp.85-98
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• 2021

The pandemic of COVID-19 further promoted the imbalance in the volume of imports and exports among countries using containers, which worsened the shortage of empty containers. Since it is important to secure as many empty containers as the appropriate demand for stable and efficient port operation, measures to predict demand for empty containers using various techniques have been studied so far. However, it was based on long-term forecasts on a monthly or annual basis rather than demand forecasts that could be used directly by ports and shipping companies. In this study, a daily and weekly prediction method using an actual artificial neural network is presented. In details, the demand forecasting model has been developed using multi-layer perceptron and multiple linear regression model. In order to overcome the limitation from the lack of data, it was manipulated considering the business process between the loaded container and empty container, which the fully-loaded container is converted to the empty container. From the result of numerical experiment, it has been developed the practically applicable forecasting model, even though it could not show the perfect accuracy.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.269-282
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• 2009

This study investigates the difference of behavioral patterns between the import container volume of all ports and that of Gwangyang port in Korea. All series span the period January 1999 to December 2008. I first test whether the series are stationary or not. I can reject the null hypothesis of a unit root in each of the level variables and of a unit root for the residuals from the cointegration at the 5 percent significance level. I hitherto make use of variance decompositions and impulse response functions, both of which have now been widely used to examine how much movement in one variable can be explained by innovations in different variables and how rapidly these fluctuations in one variable can be transmitted to another. The variance decompositions for the import container volume show that the proportions of the forecast error variance of import container volumes explained by themselves are 30 and 26 per cent after 12 months, respectively. As a result, innovations in exchange rate and business activity explain 70 and 74 per cent of the variance in the import container volume. All in all, innovation accounting indicates that import container volumes are not exogenous with respect to exchange rate and business activity. The impulse responses indicate that container volumes decrease sharply to the shocks in exchange rate and decay very slowly to its pre-shock level, while container volumes respond positively to the shocks in the business activity and disappear very slowly, showing that the shocks last very long. Furthermore Gwangyang port is more sensitive to the change of the exchange rate and the industrial production than all ports.