• Title/Summary/Keyword: Freight Demand Analysis

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Strategies to Attract Transshipment Container Cargoes among Main Competitive Ports in North (East Asian Region) (동북아 경쟁항만간의 환적화물 유치전략 (부산항을 중심으로))

  • 정태원;곽규석
    • Journal of Korean Society of Transportation
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    • v.20 no.7
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    • pp.43-50
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    • 2002
  • Competition to attract the increasing container cargoes of North China and the West Japan in North-East Asia region is fairly intensed in recent days between the main ports of Korea, China, and Japan. Inducing a new container cargo make those countries possible to invest enormous fund to mordernize its port facilities, as well as to improve efficiency in Port operation and management. In this situation, Strategy to attract transshipment cargoes is of the immediate necessity, This study, therefore, aims to establish the feasible strategies to attract transshipment container cargoes in the North-East Asian region by empirical analysis, he major output of the research is as follows : First, Busan Port to attract transshipment cargoes is required to adjust port tariff and free storage period with flexibility for liner shipping companies and freight forwarder. Second, Price-Demand function of Busan port between main competitive ports in North-East Asian region that is derived from strategies to attract transshipment cargoes, helps marketing manager to fix scientifically port price as understanding the change of demand quantity.

Structural Analysis of the Governing Variables Affecting the Structural Strength Evaluation of the Lashing Bridges in Container Vessels (컨테이너선 라싱 브릿지 구조 강도 평가에 영향을 미치는 주요 변수의 구조해석)

  • Myung-Su Yi;Joo-Shin Park
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.2
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    • pp.230-237
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    • 2023
  • Due to the COVID-19 pandemic and climate change, shortages of essential commodities and resources continue to occur globally. To address this problem, trade volume demand suddenly increased, driving up the freight rate of container ships sharply. The size of container vessels progressively increased from 1,500 TEU (twenty-foot equivalent unit) in the 1960s to 24,400 TEU in 2021. As the improvement of container loading capacity is closely related to the enlargement of the lashing bridge structure, it is necessary to design a structure effective for good container securing and safe under the various external loads that occur during voyage. Major classification societies have recently issued structural-analysis-based guidelines to evaluate the structural safety of lashing bridges, but their acceptance criteria and evaluation methods are different, causing confusion among engineers during design. In this study, the strength change characteristics are summarized by variations in the main variables (modeling range, opening consideration, mesh size) likely to affect the results. Based on this result, the authors propose a reasonable structural-analysis-based evaluation that is expected to serve as a reference in the next revision of classification standards.

Time series and deep learning prediction study Using container Throughput at Busan Port (부산항 컨테이너 물동량을 이용한 시계열 및 딥러닝 예측연구)

  • Seung-Pil Lee;Hwan-Seong Kim
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2022.06a
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    • pp.391-393
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    • 2022
  • In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

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The Effect of Baltic Dry Index on the Korean Stock Price Volatility (발틱운임지수가 한국 주가 변동성에 미치는 영향)

  • Choi, Ki-Hong;Kim, Dong-Yoon
    • Journal of Korea Port Economic Association
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    • v.35 no.2
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    • pp.61-76
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    • 2019
  • The purpose of this study is to use the EGARCH model and Granger causality test to analyze how the change in the BDI affects the Korean stock price volatility. The main analysis results are summarized as follows. First, according to the results of the mean equation, the change in the BDI is significant in large-cap stocks, as well as in the manufacturing, service, and chemistry indexes, but not in others. This implies that the Korean stock market does not respond appropriately to the maritime market situation; further, the increase in demand for raw materials has not led to a real economic recovery. Second, in the result of the variance equation, the coefficient on the change in the BDI is negative(-), and the change in the BDI is significant for all size indexes. Particularly, the change in the BDI has a greater impact on the volatility of small-cap stocks than that of large-cap stocks. The results of the analysis of the sector indexes were statistically significant for the service, financial, construction, and electric and electronics industries, but not for the manufacturing and chemical industries. In particular, the changes in the BDI have the greatest impact on the construction industry. Third, according to the Granger causality test results, the change in the BDI leads the financial industry and construction industry. There is, however, no relationship between the BDI and the other indexes. This shows that change in the shipping freight index can be used to predict the volatility in the Korean stock market. This can help investors and policymakers make better decisions.

Truck Destination Choice Behavior incorporating Time of Day, Activity duration and Logistic Activity (출발시간, 통행거리 및 물류활동 특성을 고려한 도착지 선택행태분석)

  • Sin, Seung-Jin;Kim, Chan-Seong;Park, Min-Cheol;Kim, Han-Su
    • Journal of Korean Society of Transportation
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    • v.27 no.1
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    • pp.73-81
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    • 2009
  • While various factors in passenger and freight demand analysis affect on destination choice, a key factor, in general. is an attractiveness measure by size variable (e.g., population. employment etc) in destination zone. In order to measure the attractiveness, some empirical studies suggested that disaggregate gravity model are more suitable than aggregate gravity model. This study proposes that truck travelers trip diary data among Korean commodity flow data could be used to estimate the behaviors of incorporating trip departure time, activity duration and attractiveness in destination. As a result, the main findings of size and distance variables coincide with the conventional gravity model having a positive effect of population variable and a negative effect of distance variable. Due to disaggregate gravity modeling, the unique findings of this study reports that small trucks are more likely to choose short distance and early morning, morning peak and afternoon peak departure time choice. On the other hand, large trucks are more likely to choose long distance and night time departure time choice.

The Economic Cycle and Contributing Factors to the Operating Profit Ratio of Korean Liner Shipping (경기순환과 우리나라 정기선 해운의 영업이익률 변동 요인)

  • Mok, Ick-soo;Ryoo, Dong-keun
    • Journal of Navigation and Port Research
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    • v.46 no.4
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    • pp.375-384
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    • 2022
  • The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.