• 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 Navigation and Port Research
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• v.46 no.5
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• pp.449-457
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• 2022

The Port of Busan is currently ranked as the seventh largest container port worldwide in terms of cargo throughput. However, port competition in the Far-East region is fierce. The growth rate of container throughput handled by the port of Busan has recently slowed down. In this study, we analyzed how economic conditions and multiple external shocks could influence cargo throughput and identified potential implications for port business. The aim of this study was to build a model to accurately forecast port throughput using the ARIMA model, which could incorporate external socio-economic shocks, and the VEC model considering causal variables having long-term effects on transshipment cargo. Findings of this study suggest that there are three main areas affecting container throughput in the port of Busan, namely the Russia-Ukraine war, the increased competition for transshipment cargo of Chinese ports, and the weaker growth rate of the Korean economy. Based on the forecast, in order for the Port of the Port of Busan to continue to grow as a logistics hub in Northeast-Asia, policy intervention is necessary to diversify the demand for transshipment cargo and maximize benefits of planned infrastructural investments.

• Journal of Navigation and Port Research
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• v.44 no.1
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• pp.44-52
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• 2020

More accurate forecasting of port cargo in the global long-term recession is critical for the implementation of port policy. In this study, the Busan Port container volume (export cargo and transshipment cargo) was estimated using the Vector Autoregressive (VAR) model and the vector error correction (VEC) model considering the causal relationship between the economic scale (GDP) of Korea, China, and the U.S. as well as ARIMA, a single volume model. The measurement data was the monthly volume of container shipments at the Busan port J anuary 2014-August 2019. According to the analysis, the time series of import and export volume was estimated by VAR because it was relatively stable, and transshipment cargo was non-stationary, but it has cointegration relationship (long-term equilibrium) with economic scale, interest rate, and economic fluctuation, so estimated by the VEC model. The estimation results show that ARIMA is superior in the stationary time-series data (local cargo) and transshipment cargo with a trend are more predictable in estimating by the multivariate model, the VEC model. Import-export cargo, in particular, is closely related to the size of our country's economy, and transshipment cargo is closely related to the size of the Chinese and American economies. It also suggests a strategy to increase transshipment cargo as the size of China's economy appears to be closer than that of the U.S.

• 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 Korean Port Research
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• v.15 no.1
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• pp.19-26
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• 2001

The purpose of this study is to improve the strategies for transportation systems of container cargoes in Busan port. Therefore, container cargoes forecasting is done through logistic methods based on past trends. In 2011, container cargoes demand was forecasted 8.791 million TEU(T/S including 12.559 million TEU). In order to improve transportation systems of container cargoes, the conclusions of this study can be summarized as follows ; \circled1 port facilities expansion, \circled2 diversity of container transport modes, \circled3 make up ICD and exclusive container roads, \circled4 the second Seoul-Busan Expressway.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• 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.