• 한국벤처창업학회:학술대회논문집
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• 2007.04a
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• pp.187-199
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• 2007

항만의 개발은 투자시점에서 10여년이 소요되는 대규모 자본과 시간이 소요되는 사업이므로 항만 물동량을 사전에 정확히 예측하지 못하면, 과잉투자, 중복투자 또는 기관시설이 부족하는 등 큰 문제에 봉착하여 진다. 항만 물동량 예측은 항만 개발에 앞서 매우 중요한 과제이다. 따라서 본 논문에서는 파워심 프로그램을 활용한 항만 물동량 예측 시뮬레이터 개발에 앞서 기초 연구단계로 항만 물동량 발생 요소들의 관계를 정립하고 인과관계를 시스템 다이내믹스 기법을 이용하여 밝혔다. 이 시뮬레이터는 항만 물동량 예측 등 관련 산업기술 발전에 기여하리라 전망된다.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.1-17
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• 2008

In this paper the cargo transportation were forecasted for the North Port in Busan through time series models. The cargo transportation were classified into three large groups; container, oil, general cargo. The seasonal indexes of existing cargo transportation were firstly calculated, and optimum models were chosen among exponential smoothing models and ARIMA models. The monthly cargo transportation were forecasted with applying the seasonal index in annual cargo transportation expected from the models. Thus, the cargo transportation in 2011 and 2015 were forecasted about 22,900 myriad ton and 24,654 myriad ton respectively. It was estimated that container cargo volume would play the role of locomotive in the increase of the future cargo transportation. On the other hand, the oil and general cargo have little influence upon it.

• 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 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.

• 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 Korea Port Economic Association
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• v.26 no.1
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• pp.61-82
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• 2010

The purpose of this study is to examine the causuality among export and import trading volume of port of Busan, Kwangyang, Incheon and to induce policy implications. 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 Granger causality and impulse response and variance decomposition based on VECM. The results indicate that the trading volume of port of Busan is not largely influenced by that of port of Kawangyang and Incheon, but the trading volume of port of Kawangyang and Incheon is largely influenced by other ports including port of Busan. The result suggest that government has to focus on policy that the port of Kawangyang and Incheon can raise its own competitiveness in the world market.

• 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.

• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.255-273
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• 2014

The purpose of this paper is to examine the causal relationship among the trading volume of Pyeongtaek port, Incheon Inner Harbor, Incheon North Harbor. Methodologically, Granger causality, impulse response function, and variance decomposition based on VAR are used. The results indicate that Pyeongtaek port trading volume positive shock has positive effects on Incheon North Harbor. In addition, Incheon Inner Harbor trading volumes positive shock has negative effects on Pyeongtaek port. The results also suggest that the volume of Pyeongtaek port Granger-causes the volume of Incheon North Harbor, but not vice versa. The volume of Incheon Inner Harbor Granger-causes the volume of Pyeongtaek port. Based on these results, we suggest that port authorities have to focus on policies that would promote copetition between port of Pyeongtaek and Incheon in the world harbor industry.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.179-196
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• 2021

Port congestion rate at Busan Port has increased for three years. Port congestion causes container reconditioning, which increases the dockyard labor's work intensity and ship owner's waiting time. If congestion is prolonged, it can cause a drop in port service levels. Therefore, this study proposed an anomaly detection method using ARIMA(Autoregressive Integrated Moving Average) model with the daily volume data from 2013 to 2020. Most of the research that predicts port volume is mainly focusing on long-term forecasting. Furthermore, studies suggesting methods to utilize demand forecasting in terms of port operations are hard to find. Therefore, this study proposes a way to use daily demand forecasting for port anomaly detection to solve the congestion problem at Busan port.

• 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.