• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.165-185
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• 2011

Transshipment traffic has significant meanings because it gives positive effects on increasing the container handling volumes in seaports, and revitalizes the regional and national development. Korean container port's transshipment traffic volumes, however, will slowly decrease due to the direct ships' calling into Chinese ports, which recently has a huge development plan. There are a lot of stress on forecasting the transshipment traffic volumes because the Korean container port development plans are designed based on this container traffic which consists of import and export traffic, and transshipment traffic. The transshipment traffic volumes are assumed to occupy 40% of total container traffic volumes. Despite of the importance of forecasting the transshipment traffic, a little studies are suggested using the concepts of the port competitiveness. In this respect, this study aims to estimate the Port Competitiveness Index and Transshipment traffic Volumes using the System Dynamics methodology. As a result, transshipment traffic volumes are predicted as: 20 million TEUs in Korea under the 4% annual increasing rates, 90 million TEUs in China under the 6% annual increasing rates, and 2.5 million TEUs in Japan under the 1% annual increasing rates respectively. The suggested results can be used to enhance the container port competitiveness and produce more transshipment traffic volumes.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.905-910
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• 2012

In early 1990s, the Sea & Air Transport Cargoes (SATC) was increased annually with more than 50% rate due to the rising trade between Korea and China. However, after that, the increasing rate of the SATC was slowdown from the late 1990s, furthermore, recently it became sluggish and declined. This phenomenon is totally different compared to the skyrocketing trade volumes between two countries. In this respect, to forecast the SATC, draw out the factors for activation, and calculate the weight of priority of these factors are urgently needed. To achieve the research objectives, the ARIMA and Fuzzy-AHP were used as research methodology. The estimated volume of SATC using the data from year 2007 to 2012 on the ARIMA model, will be reached approximately 33,000 tons in year 2015. In the mean time, For drawing out and weighing the activation factors for SATC, the Fuzzy-AHP was adopted. As a result, 'Sea & Air transportation-related information system policies' is the most important factor among the principle criteria, and 'the construction of consolidation logistics center' is the most important factor among the 12 sub-principle criteria.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.69-84
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• 2015

The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.10a
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• pp.187-188
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• 2009

On this study, the artificial neural network, one of the nonlinear forecasting methods, is compared with ARIMA model through performing a forecast of container traffic. The existing studies have been used the rule of thumb in topology design for network which had a great effect on forecasting performance of the artificial neural network. However, this study applied the genetic algorithm, known as the effectively optimal algorithm in the huge and complex sample space, as the alternative.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.61-69
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• 2015

This study compares the forecasting accuracy of five econometric models on domestic total freight volume in South Korea. Applied five models are as follows: Ordinary Least Square model, Partial Adjustment model, Reduced Autoregressive Distributed Lag model, Vector Autoregressive model, Time Varying Parameter model. Estimating models and forecasting are carried out based on annual data of domestic freight volume and an index of industrial production during 1970~2011. 1-year, 3-year, and 5-year ahead forecasting performance of five models was compared using the recursive forecasting method. Additionally, two forecasting periods were set to compare forecasting accuracy according to the size of future volatility. As a result, the Time Varying Parameter model showed the best accuracy for forecasting periods having fluctuations, whereas the Vector Autoregressive model showed better performance for forecasting periods with gradual changes.

• Journal of Korea Port Economic Association
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• v.24 no.3
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• pp.57-77
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• 2008

The aim of this paper is to make projection of the demand for export loaded container throughput originating at Incheon port in Korea over the period in question. Systematic analysis is used as a forecasting method instead of quantitative analysis. First of all, the method explores coincident indicators which may reflect the square measure of neighboring industrial complexes which originate/destinate general cargo in export traffic trends. It is noted that in terms of the export loaded container throughput, per unit production scale is intermediated transforming from square measure of production facilities to freight weight in Korea. Consequently, the future progress of the volume can be anticipated relying on the development schemes for developing square measure out of the total square of the industrial complexes. Thus, moving-into percentage of the industrial complexes, percentage of business categories, percentage of capacity and percentage of passing through via Incheon port are adopted and the future traffic demand is projected taking advantage of them.

• Journal of Korea Port Economic Association
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• v.33 no.3
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• pp.17-34
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• 2017

This study aims to analyze the freight transport demand and benefit for the introduction of an automated freight transport system focusing on the Global Industry and Logistics City (GILC) in Busan. In pursuit of this aim, four alternatives were calculated - using the freight volume estimating methods and included, the number of businesses, the number of employees set up, future estimated cargo volume, and switched volume from other transport modes into the GILC. Economic benefits were analyzed against social benefits and costs accordingly. The result of the freight transport demand forecast found, the cargo volume of "Alternative 2-1" to be the most advantageous, applying the number of employee unit method and proportion of employees in Gangseo-gu, Busan. In addition to the conventional analysis of direct benefit items (reduction of transport time, traffic accidents and environmental costs), this study also considered additional benefit items (congestion costs savings, and road maintenance costs in terms of opportunity cost). It also considered advanced value for money research in guidance on rail appraisal of U.K, Federal Transport Infrastructure Plan 2003 of Germany, and RailDec of the United States. The study aims to further contribute to estimating minimum cargo transport demands and assess the economic feasibility of the introduction of new intermodal automated freight transport systems in the future.

• Journal of Korea Port Economic Association
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• v.28 no.4
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• pp.193-207
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• 2012

The of purpose of this study is to predict export and import container volumes using a Decision Tree analysis. Factors which can influence the volume of container cargo are selected as independent variables; producer price index, consumer price index, index of export volume, index of import volume, index of industrial production, and exchange rate(won/dollar). The period of analysis is from january 2002 to December 2011 and monthly data are used. In this study, CRT(Classification and Regression Trees) algorithm is used. The main findings are summarized as followings. First, when index of export volume is larger than 152.35, monthly export volume is predicted with 858,19TEU. However, when index of export volume is between 115.90 and 152.35, monthly export volume is predicted with 716,582TEU. Second, when index of import volume is larger than 134.60, monthly import volume is predicted with 869,227TEU. However, when index of export volume is between 116.20 and 134.60, monthly import volume is predicted with 738,724TEU.