• Journal of Korean Society of Transportation
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• v.19 no.2
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• pp.29-40
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• 2001

부산항은 우라나라의 전체 수출입 및 환적 컨테이너 물동량의 약 90%를 처리하고 있기 때문에 지금까지 꾸준한 물동량 증가 추세를 보이고 있다. 컨테이너 물동량의 증가에 따라 정부에서는 항만시설을 지속적으로 확충하여 컨테이너 처리능력을 확대하고 있으나, 컨테이너 물동량의 증가율이 컨테이너 처리시설 확보율을 초과하여 부산항 컨테이너 전용부두의 컨테이너 수용능력은 부족한 실정이다. 이와 같은 컨테이너 장치장 부족문제를 해결하기 위해서 그 동안 부산항의 ODCY에서 처리하였으나, 최근 부두밖 장치장의 단계적 이전 및 폐쇄방침이 결정됨에 따라 부산항의 장치장 부족문제가 대두되고 있는 실정이다. 따라서 본 연구에서는 장치장 부족문제를 해결하고 부산항 컨테이너 유통체제를 개선시킬 수 있는 방안을 제시하는데 목적을 두고 있다. 이를 위하여 첫째, 부산항의 컨테이너화물 유통 현황 및 문제점을 분석하고 둘째, 부산항 컨테이너화물 유통체제의 개선대안을 설정하여 분석결과를 제시한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.166-169
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• 2011

항만간 허브항 경쟁이 극심해 지고 있는 오늘날, 컨테이너 선사는 M&A 및 전략적 제휴로 컨테이너터미널 운영사와의 가격 협상력의 우월적 지위를 갖게 되어 컨테이너터미널 운영사간 선사 및 화물유치를 경쟁을 더욱 부추기고 있다. 그러나 수요측면에서 컨테이너물동량 증가율 둔화로 컨테이너터미널에서 처리해야 할 물동량은 한정되어 있는 반면, 공급 측면에서 항만터미널의 지속적인 건설은 항만간 또는 터미널간 물량 유치경쟁을 과열시키고 있다. 특히 부산항은 신항 개장이후 북항과 신항간 물동량 유치경쟁으로 인하여 항만하역시장의 교란을 가져오고 있다. 본 연구에서는 부산항 컨테이너 항만하역시장의 구조적 특성분석과 설문조사 방법론을 통하여 향후 부산항 항만하역시장의 안정화 방안을 제시하고자 한다. 시장구조 분석결과 부산항은 한정된 처리물량과 신항의 개장으로 인한 공급과잉, 그리고 정부의 지역항만개발정책에 따른 컨테이너화물의 분산처리로 지속적인 부산항의 비중 감소로 선석당 처리물량이 감소하고 있다. 이에 따라 선사의 우월적 지위를 이용한 하역료 인하요구로 터미널운영사간에 서비스경쟁이 아닌 비협력적인 가격경쟁으로 재정수지가 악화되고 있고, 또한 '10년 외국적선사의 처리물량이 60%를 차지하고 있어 국부유출이 심각한 실정이다. 따라서 하역시장 안정화 방안으로 항만시설 수요 및 공급의 불균형을 조정하기 위하여 항만풀링공동기금관리를 통한 재정수지를 확보할 수 있는 항만풀링제도를 제안하며, 이 제도의 운영을 위하여 한시적으로 컨테이너터미널 운영사별 처리물량 상한제를 도입하는 것이 바람직하다고 판단된다.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.1-23
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• 2013

This paper estimates and forecasts the container throughput of Busan port using the monthly data for years 1992-2011. To do this, this paper uses the several seasonal multiplicative ARIMA models. Among several ARIMA models, the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$ is selected as the best model by AIC, SC and Hannan-Quin information criteria. According to the forecasting values of the selected seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$, the container throughput of Busan port for 2013-2020 will increase steadily annually, but there will be some volatile variations monthly due to the seasonality and other factors. Thus, to forecast the future container throughput of Busan port and to develop the Busan port efficiently, we need to use and analyze the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$.

• 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 Korean Society of Transportation
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• v.20 no.7
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• pp.51-58
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• 2002

The purpose of this paper is to find some implications for Korean seaports in terms of operation and development of ports, in particular for attracting more transshipment container cargoes into major Korean seaports. This was accomplished by the O-D analysis between major Korean seaports and top 20 Chinese ports.

• 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.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.529-538
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• 2018

The port industry is an important national industry which significantly affects Korea's imports and exports which are centered on economic structure. For instance, the Port of Busan, which handles 75% of domestic container freight volume, is expected to become increasingly critical for container liner routes. For this reason, there have been continued efforts to expand freight service to attract international freight volume. This study analyzes the structural characteristics of the port network connected to the Port of Busan by analyzing the pattern of the container liner route from 2012 to 2016 by using social network analysis. According to the Port of Busan's liner route network, the port with the highest degree of centrality, closeness centrality, and betweenness centrality was found to be the Port of Singapore. The comparison of Busan's annual container handling rank by countries and the port center network analysis of Port of Busan rank was found to be different. As a result, it was established that China's East Port, which occupies a high percentage of the volume of cargo handled by Port of Busan, is not a hub port of Busan when viewed on the Busan's container terminal liner network. In addition, even if the number of Port of Busan container liner service increases, it is estimated that the vessels to be added to the fleet will be limited to small to medium sized, or that Busan port has characteristic of a feeder port for the Port of Singapore, according to the network.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.133-134
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• 2009

선박의 대형화와 함께 해운 항만 시장이 급속히 변화하는 가운데 각 항만들은 항만 경쟁력을 가지기 위해 물동량 예측과 더불어 하역료를 바탕으로 한 부두사용료 수준에 대해 검토를 시행하고 있는 실정이다. 또한 부산북항 재개발과 관련하여 일반부두 폐쇄 및 터미널의 이전이 계획되어지는 가운데, 터미널 임대료 및 물동량 배분에 관한 연구가 활발하게 이루어지고 있다. 따라서 본 논문은 컨테이너 터미널의 주변여건 변화에 따른 컨테이너화물 물동량을 추정 및 예측하고, 기존 사용료 및 부산북항의 특정 터미널을 대상으로 향후 2020년까지의 사용료를 검토하고자 한다.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.25-37
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• 2016

The economy of South Korea has experienced two financial crises: the 1997 Asian financial crisis and the 2008 global financial crisis. These crises had a significant impact on the nation's macro-economic indicators. Furthermore, they had a profound influence on container traffic in container ports in Busan, which is the largest port in South Korea in terms of TEUs handled. However, the impact of the Asian financial crisis on container throughput is not clear. In this study, we assume that the two financial crises are independent and different, and then analyze how each of them impacted container throughput in Busan ports. To perform this analysis, we use an intervention model that is a special type of ARIMA model with input series. Intervention models can be used to model and forecast a response series and to analyze the impact of an intervention or event on the series. This study focuses on the latter case, and our results show that the impacts of the financial crises vary considerably.

• 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.