• Journal of Korea Port Economic Association
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• v.26 no.3
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• pp.18-41
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• 2010

The purpose of this paper is to show the empirical measurement way for predicting the seaport efficiency by using Super SBM(Slack-based Measure) with Wilcoxson signed-rank test under CRS(constant returns to scale) condition for 20 Korean ports during 11 years(1997-2007) for 3 inputs(port investment amount, birthing capacity, and cargo handling capacity) and 5 outputs(Export and Import Quantity, Number of Ship Calls, Port Revenue, Customer Satisfaction Point for Port Service and Container Cargo Throughput). The main empirical results of this paper are as follows. First, Super SBM model has well reflected the real data according to the Wilcoxon signed rank test, because p values have exceeded the significance level. Second,Super-SBM has shown about 87% of predicting ratio for the ports efficiency and the optimal size of investment in domestic seaport. The policy implication to the Korean seaports and planner is that Korean seaports should introduce the new methods like Super-SBM method with Wilcoxon signed rank test for predicting the efficiency of port performance and the optimal size of investment as indicated by Panayides et al.(2009, pp.203-204).

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.617-618
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• 2020

우리나라의 지리적인 여건상 대륙과 연결되지 않기 때문에 해상운송에 절대적으로 의존하고 있다. 해상운송에 있어 항만시설의 확보가 필요하며 대외무역의존도가 높은 우리나라의 경우 더욱 중요한 역할을 한다. 항만시설은 장기적인 항만수요예측을 통해 대규모 인프라투자를 결정하며 단기적인 예측은 항만운영의 효율성을 개선하고 항만의 경쟁력을 제고하는데 기여하므로 예측의 정확성을 높이기 위해 많은 노력이 필요하다. 본 논문에서는 딥러닝 모델 중에 하나인 LSTM(Long Short Term Memory)을 적용하여 우리나라 주요항만의 컨테이너 물동량 단기예측을 수행하여 선행연구들에서 주류를 이뤘던 ARIMA류의 시계열모델과 비교하여 예측성능을 평가할 것이다. 본 논문은 학문적으로 항만수요예측에 관한 새로운 예측모델을 제시하였다는 측면에서 의미가 있으며 실무적으로 항만수요예측에 대한 정확성을 개선하여 항만투자의사결정에 과학적인 근거로서 활용이 가능할 것으로 기대된다.

• Journal of Korea Port Economic Association
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• v.24 no.4
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• pp.313-327
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• 2008

The purpose of this paper is to show the empirical measurement way for predicting the seaport efficiency by using SBM with Wilcoxson signed-rank test under CRS(constant returns to scale) condition for 20 Korean ports during 1994-2003 for 2 inputs(birthing capacity, cargo handling capacity) and 3 outputs(Export and Import Quantity, Number of Ship Calls, Port Revenue). The main empirical results of this paper are as follows. First, forecasting data have well reflected the real data according to the Wilcoxon signed rank test, because p values have exceeded the 0.05 significance level. Second, SBM has shown the effectiveness for predicting the ports efficiency even though the predicting powers are different according to the levels of p values. The policy implication to the Korean seaports and planner is that Korean seaports should introduce the new methods like SBM method with Wilcoxon signed rank test for predicting the port performance and enhancing the efficiency.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.236-238
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• 2023

자율운항선박 기술개발 사업중 해운 6세부(자율운항시스템 원격관리 및 안전운영 기술 개발)과제에서 자율운항 선박을 지원하기 위한 6종 서비스 중 항만 효율화를 위한 양·적하 작업 시간 예측 서비스에 대한 연구 및 개발을 목표로 한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.155-161
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• 2023

97.5% of our country's exports and 87.2% of imports are transported by sea, making ports an important component of the Korean economy. To efficiently operate these ports, it is necessary to improve the short-term prediction of port water volume through scientific research methods. Previous research has mainly focused on long-term prediction for large-scale infrastructure investment and has largely concentrated on container port water volume. In this study, short-term predictions for petroleum and liquefied gas cargo water volume were performed for Ulsan Port, one of the representative petroleum ports in Korea, and the prediction performance was confirmed using the deep learning model LSTM (Long Short Term Memory). The results of this study are expected to provide evidence for improving the efficiency of port operations by increasing the accuracy of demand predictions for petroleum and liquefied gas cargo water volume. Additionally, the possibility of using LSTM for predicting not only container port water volume but also petroleum and liquefied gas cargo water volume was confirmed, and it is expected to be applicable to future generalized studies through further research.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1387-1390
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• 2021

'체선'은 입항한 선박이 하역하지 못하고 대기하는 상황을 나타내는 말이며 이는 항만의 비효율성을 초래하고 금전적인 손실을 가져다준다. 따라서 본 논문에서는 기존 관제정보 API를 활용하여 체선율 분석 및 예측 시스템을 제안하고자 한다. 이를 통해 항만 시설 관리자에게는 투자방향성을 제공하고, 선박과 화주에게는 물류효율성을 제공해 줄 것으로 기대된다.

• 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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• 2022.11a
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• pp.344-346
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• 2022

최근 자율운항기술개발이 활발하게 이루어짐에 따라 자율운항선 실증이 증가하고 있으며, 또한 자율운항선의 효율적 운용 특히 운항의 안전성을 위해 입출항 시기의 적절성 또한 중요해지고 있다. 이에 해상교통혼잡도를 예측하고자 하였고, AIS 빅데이터를 통해 선박별항적을 분석 및 분류하고자 하였다. 장기적 관점에서 PORT-MIS 선박입출항현황 데이터(호출번호, 입항일시, 출항일시, 전출항지, 차항지, 계선지)를 과거 AIS 빅데이터와 연결시켜 과거 항적 중 가장 가까운 항적을 찾고자 하였다. 그리고 당시 소요 시간을 반영하여 12개의 시간대별로 어느 시점에 어느 위치 구간에 선박들이 놓이게 될지 예측하였고, 특히 입출항 시기의 적절성에 핵심이 되는 13개로 모델링된 영역에 몇 개의 선박들이 항로를 지나는지에 따라 혼잡도(원활, 혼잡, 정체)를 구분하였다. 또한, 본 연구에서는 단기적 관점에서 실제 AIS가 수신된 후에도 유사한 항적을 검사해가며 혼잡도를 예측하고자 하였고, 이러한 장단기적 혼잡도 예측을 통해 미래 자율운항선입출항 지원 서비스의 안전과 그 적절성을 제공하고자 하였다.

• Journal of Korea Port Economic Association
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• v.25 no.4
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• pp.131-146
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• 2009

The purpose of this study is to predict the railway cargo volume on Busan new-port and north-port, in order to revitalize railway transport. This paper is organized as follows. Section 1 presents the description of the objective and methods on this study. Section 2 presents the status of Railway Cargo volumes and Construction plan of railway facilities in Busan New port. Section 3 presents the Forecast Railway Cargo volume using a volume ratio, actual volume records and another predicted datas. Section 4 summarizes our conclusions and further research topics. Especially, korea faces enforcement of green Logistics policy. Modal shift to trail freight transportation is one of ways, but there are no more detail plans. so it need that a cooperation system in government department, a indirect subside policy shift to rail freight transportation from trucking for revitalization of Railway Freight transportation.

• Journal of Korea Port Economic Association
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• v.23 no.4
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• pp.159-175
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• 2007

The purpose of this study is to examine the causal relationship between trading volume of freight and industrial growth in Korea ports, 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 based on an error correction model, Hsiao(1981) method and variance decomposition. The results indicate that the extent of causality between trading volume of freight and industrial growth is strong in order of Incheon port, Busan port, Gwang Yang port, Ulsan port. We can infer policy suggestions as follows; The port policy of government must be focused on re-adjusting investment among Korea ports and raising competitive power of Korea ports