• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.175-177
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• 2014

본 연구는 부산항에서 차지하는 환적물동량의 위상을 고려하여 환적화물에 대한 정확한 예측을 위한 모델을 수립하는데 그 목적이 있다. 환적물량을 결정짓는 요소로는 부산항의 경쟁력 뿐 아니라 중국 등의 수출입 물동량 증가량과 중국항만의 경쟁력도 중요요소이며, 이들 요소들이 상호간에 영향을 주고 받음에 따라 그러한 순환적 인과관계 분석에 적합한 시스템 다이내믹스(SD) 기법을 활용하여 환적화물에 대한 예측을 시도해 보고자 한다.

• 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 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 Navigation and Port Research
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• v.32 no.1
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• pp.81-88
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• 2008

The forecast of a container traffic has been very important for port plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate that effectiveness can differ according to the characteristics of ports.

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.701-708
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• 2014

This study is aimed to obtain the influence of port hinterlands on container cargo volumes of Incheon port using System Dynamics(SD). Also, macro economic index such as exchange rates(US dollar), balance of current account, capital balance, Japan trade, China trade, export unit value index, import unit value index, total turnover of Incheon port were used as the factors that influence container cargo volumes of Incheon port. Moreover micro index regarding port hinterlands' operating companies such as total sales, rental fee, number of employees were introduced in the simulation model. In order to measure accuracy of the simulation, this study implemented MAPE analysis. And after the implementation, the simulation was decided as a much more accurate model because MAPE value was calculated to be within 10%. This study respectively examined factors using the sensitivity analysis. As a result, in terms of the effects on cargo volume in Incheon Port, the factor named 'cargo volumes of port hinterlands' operating companies' is most significant. And increasing the rental fee of hinterland was resulted in decreasing the cargo volumes of Incheon port.

• Journal of Korean Port Research
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• v.11 no.1
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• pp.1-11
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• 1997

The conventional methods of container forecasting is done through regression methods based on GNP growth trends and by other forecasting methods proposed by several authors. However these efforts prove to be inadequate with visible weakness and a more reasonable approach need to be determined. The succeeding sections elaborate the methodology and approach adopted. The results are then compared through a case study involving the forecast figures derived by the Pusan Port Authority and the values obtained by MRCS model introduced in this paper.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.295-303
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• 2017

In this paper, we analyze the trends of the international shipping market and the domestic and foreign factors of the crisis of the domestic shipping market, and identify the characteristics of the recovery of the Busan New Port trade volume which has decreased since the crisis of the domestic shipping market We quantitatively analyzed the future volume of Busan New Port and analyzed the trends of the prediction and recovery trends. As a result of analyzing Busan New Port container cargo volume by using big data analysis tool R, the variation of Busan New Cargo container cargo volume was estimated by ARIMA model (1,0,1) (1,0,1)[12] Estimation error, AICc and BIC were the most optimal ARIMA models. Therefore, we estimated the estimated value of Busan New Port trade for 36 months by using ARIMA (1, 0, 1)[12], which is the optimal model of Busan New Port trade, and estimated 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, and 4,526,406 TEU, respectively, indicating that it increased by about 2%, 2%, and 1%.

• Journal of Korea Port Economic Association
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• v.34 no.1
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• pp.81-98
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• 2018

Port logistics is essential for Korea's economy which heavily rely on international trade. Vast amounts of capital and time are consumed for the operation and development of ports to improve their competitiveness. Therefore, it is important to forecast cargo volume in order to establish the optimum level of construction and development plan. Itemized forecasting is necessary for appropriate port planning, since disaggregate approach is able to provides more realistic solution than aggregate forecasting. We introduce a new time series model which is Two-way Seasonality Multiplied Regressive Model (TSMR) to forecast oil cargo volume, which accounts for a large portion of total cargo volume in Korea. The TSMR model is designed to take into account the characteristics of oil cargo volume which exhibits trends with short and long-term seasonality. To verify the TSMR model, existing forecasting models are also used for a comparison reason. The results shows that the TSMR excels the existing models in terms of forecasting accuracy whereas the TSMR displays weakness in short-term forecasting. In addition, it was shown that the TSMR can be applied to other cargoes that have trends with short- and long-term seasonality through testing applicability of the TSMR.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.259-260
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• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.

• Journal of Navigation and Port Research
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• v.34 no.5
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• pp.397-404
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• 2010

The Currently, most cargos of container transshipment between Busan Port and New Port are transported over land, and the rest is transported by barge. This study estimated firstly the traffic between those ports through simulations in order to analyze the feasibility of container transshipment by barge. It forecasted annual profitability using determinants to affect on the barge business by the traffic, and then, discussed the feasibility. This study supposed the flexible scenarios with 50%, 60%, 80%, or 100% transshipment and the 25 monthly barge service numbers between two ports, and measured the influences of different factors according to the above various scenarios. And then the sales were evaluated by the different traffics and freights scenarios provided the business would be actually operated. Finally, Net incomes were simulated to analyze the feasibility of different scenarios by various traffics and freights. The net income should be positive to get the feasibility. To achieve this, the minimum traffic should be secured and the lowest freight per TEU should be determined. While all countries of the world is controlling CO2 emissions and emphasizes the green logistics, this study contributed to solve at the same time the problems about the pollution and the efficiency of transportation by reviewing positively the feasibility of barge transportation as an alternative to transportation overland.