• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.111-123
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• 2022

In the era of the Covid19, container shipping rates are surging up. Many studies have attempted to investigate the factors affecting a surge in container shipping rates. However, there is limited literature using text mining techniques for analyzing the underlying causes of the surge. This study aims to identify the factors behind the unprecedented surge in shipping rates using network text analysis and LDA topic modeling. For the analysis, we collected the data and keywords from articles in Lloyd's List during past two years(2020-2021). The results of the text analysis showed that the current surge is mainly due to "US-China trade war", "rising blanking sailings", "port congestion", "container shortage", and "unexpected events such as the Suez canal blockage".

• Korea Trade Review
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• v.43 no.5
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• pp.159-177
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• 2018

The container shipping sector is an important international logistics operation that connects open economies. Freight rates rapidly change as the market fluctuates, and staff related to the shipping market are interested in factors that determine freight rates in the container market. This study uses the Vector Error Correction Model(VECM) to estimate the impact of factors affecting container freight rates. This study uses data published by Clarksons. The analysis results show a 4.2% increase in freight rates when world container traffic increases at 1.0%, a 4.0% decrease in freight rates when volume of container carriers increases by 1.0%, a 0.07% increase in freight rates when bunker price increases by 1.0%, and a 0.04% increase in freight rates accompanying 1.0% increase in libor interests rates. In addition, if the current freight rate is 1.0% higher than the long-term equilibrium rate, the freight rate will be reduced by 3.2% in the subsequent term. In addition, if the current freight rate is 1.0% lower than the long-term equilibrium rate, the freight rate will decrease by 0.12% in the following term. However, the adjusting power in a period of recession is not statistically significant which means that the pressure of freight rate increase in this case is neglectable. This research is expected to contribute to the utilization of scientific methods in forecasting container freight rates.

• Spatial Information Research
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• v.20 no.3
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• pp.1-14
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• 2012

This paper has investigated the unreasonable pricing system for container cargo with special reference to inland transport since it is determined by administrative boundary without considering travelling distance. This research is primarily intended to improve the current pricing system by introducing a pricing support system about the container cargo in the country which applies GIS service area analysis to transportation data. Price simulation maps were generated to find out reasonable pricing strategy. Pricing alternative integrating travelling distance into administrative zoning brings about more desirable results than individual driving cost alone. The research can contribute to more reasonable pricing for the fuel consumption expense and serve as useful instruments for planning pricing system for container cargo. It is anticipated that this research output could be used as a valuable reference to increase the scientific and objective pricing for container cargo by overcoming serious constraints suffered from the past non-GIS-based approach.

• Journal of Korea Port Economic Association
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• v.35 no.3
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• pp.61-72
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• 2019

In order to enhance the competitiveness of the container shipping industry and promote its development, based on the empirical analyses using multivariate time series models, this study aims to suggest a few strategies related to the dynamics of the container shipping market. It uses the vector autoregressive (VAR) and vector error correction (VEC) models as analytical methodologies. Additionally, it uses the annual trade volumes, fleets, and freight rates as the dataset. According to the empirical results, we can infer that the most exogenous variable, the trade volume, exerted the highest influence on the total dynamics of the container shipping market. Based on these empirical results, this study suggests some implications for ship investment, freight rate forecasting, and the strategies of shipping firms. Concerning ship investment, since the exogenous trade volume variable contributes most to the uncertainty of freight rates, corporate finance can be considered more appropriate for container ship investment than project finance. Concerning the freight rate forecasting, the VAR and VEC models use the past information and the cointegrating regression model assumes future information, and hence the former models are found better than the latter model. Finally, concerning the strategies of shipping firms, this study recommends the use of cycle-linked repayment scheme and services contract.

• Railway Journal
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• v.18 no.5
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• pp.82-86
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• 2015

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.193-194
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• 2019

Due to structural problems in the transportation market, such as fraudulent competition and multi-level transportation transactions, tariff are not properly refIected to the final trucker even if freight tariff are increased due to diesel price hikes and transportation cost increases. By Launched "standard tariff system" in 2017 based on the trucks, government would tried to guarantee the minimum tariff such that prevents overloading, speeding, and strain for trucker. However, It is necessary to analyze in advance the issue of the system and its impact on container freight. Thus, this study analyzes the issues and countermeasures related to the tariff system that is formed when the "Truck Safety Rates" is introduced, and also analyzes the influence factors of the expenditure cost on the container freight which is applied to the "Truck Safety Rates".

• 해운
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• no.1 s.47
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• pp.14-22
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• 2008

최근들어 1만TEU급 초대형 컨테이너선이 잇따라 시장에 투입되면서 선복과잉이 우려되고 있다. 과연 메가급 컨테이너선이 규모경제의 잇점을 살릴 수 있을지, 아니면 메가급 위기로 다가올지 기대와 우려가 교차되고 있다. 다음은 로이즈 쉬핑 에코노미스트(Lloyd's Shipping Economist)지 2007년 10월호에 실린 스테판 메튜(Stephen Matthews)의 "Mega-risk"를 번역한 것이다.

• 해운
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• no.11 s.33
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• pp.43-43
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• 2006

• 해운
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• no.5 s.4
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• pp.54-56
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• 2004