• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.263-269
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• 1998

The forecasting of container cargo volumes should be estimated correctly because it has a key roles on the establishment of port development planning, and the decision of port operating system. Container cargo volumes have a dynamic characteristics which was changed by effect of competitive ports. Accordingly forecasting was needed overall approach about competitive port's development, alternation and information. But, until now, traffic forecasting was not executed according to competitive situation, and that was accomplished at the point of unit port. Generally, considering the competition situation, simulation method was desirable at forecasting because system's scale was increased, and the influence power was intensified. In this paper, considering this situation, the objectives can be outlined as follows. 1) Structural model constructs by System dynamics method. 2) Structural simulation model develops according to modelling of competitive situation by expended SD method which included HEP(Hierarchical Fuzzy Process) And actually, effectiveness was verified according to proposed model to major port in northeast asia.

• 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.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 Korea Port Economic Association
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• v.37 no.3
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• pp.1-17
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• 2021

Forecasting the daily volume of container is important in many aspects of port operation. In this article, we utilized a machine-learning algorithm based on decision tree to predict future container throughput of Busan port. Accurate volume forecasting improves operational efficiency and service levels by reducing costs and shipowner latency. We showed that our method is capable of accurately and reliably predicting container throughput in short-term(days). Forecasting accuracy was improved by more than 22% over time series methods(ARIMA). We also demonstrated that the current method is assumption-free and not prone to human bias. We expect that such method could be useful in a broad range of fields.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.323-329
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• 1998

In Pusan port, the studies, which analysis container cargo volumes by using forecasting methods and research about container logistics system, etc., have been continuously performed. But, in Pusan port, this study on an evaluation of traffic congestion has been scarcely performed until now. Especially, when changing and extending a berth, and constructing a new port, it is very important to examine this field. And it should be considered. Thus, this paper aims to analysis the effect of ship traffic condition in 2011, to evaluate marine traffic congestion, according to changing ship traffic volumes in Pusan port. To analysis it, we used simulation method and examined the results

• Journal of Korean Port Research
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• v.12 no.2
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• pp.177-194
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• 1998

In Pusan port, the studies which analyze container cargo volumes by using forecasting methods and research about container logistics system, etc., have been continuously carried out. But, in Pusan port, the study on an evaluation of traffic congestion has been scarcely performed until now. Especially, when changing and extending a berth, or constructing a new port, it is very important to examine this field. And it should be considered. Thus, this paper aims to analyze the effect of ship traffic condition in the year 2011, to evaluate marine traffic congestion according to changing ship traffic volumes in Pusan port. To analyze it, we examined the results by simulation method.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.953-955
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• 2020

공컨테이너(Empty Container)는 적컨테이너(Full Container)와 달리, 화물이 적재되지 않은 비어있는 컨테이너로 공컨테이너 재고는 수출에 비해 수입이 많은 항만에서, 수요는 수입에 비해 수출이 많은 항만에서 발생한다. 그러나 수입과 수출은 기간, 지역에 따라 유동적이기 때문에 수요와 재고량 예측에 어려움이 있는데, 본 연구에서는 자기회귀누적이동평균(ARIMA)과 머신러닝 기법을 활용하여 이를 예측하는 방법을 제시한다. 본 연구에 활용된 데이터와 프로그램 소스코드는 Kaggle 에 공개되어 있다.

• Journal of the Korean Institute of Navigation
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• v.19 no.1
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• pp.33-48
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• 1995

In the process of containerization, the problem of regional maldistribution of container management plan arises seriously due to several factors like a number of unbalances of containers between loading and discharging ports. This study focus on the minimizing cost. This study is composed of two models which in effective management decision making show decision of the number of containers and transfer of empty containers. One is decision of the number of containers which carriers should possess by appropriate forecasting and the other is effective management decision making which includes the transfer of empty containers on calling ports. This study has suggested as follows, First, the Time Series analysis method, especially the "Exponential Smooting with Trend Adjustment" was used to forecast the trade volumes for the designated traffic route. Second, the Time Series analysis method in deciding the optimal number of owned container at the unbalances trade situation between East Bound and West Bound service, most important variables were found such as total traffic volume, the calling interval at a port, the number of days of voyage and the length of stay on shore of container for the optimal number of owned container. Third, effective management decision making model, which makes it possible to analyze the impacts of change in important matters such as lease and positioning policy, and actually influence decision making.on making.