• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.703-711
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• 2018

Recently, as various metro lines have been constructed and connected to other lines the rate of utilization has also been increased. However, the existing fare policy which the government does still stick to causes not only inappropriate action to the demand pattern but aggravation of the profit of the operation company. This study reviewed rail fare policy of major countries such as Japan and U.K etc. to improve the rationality of fare policy in Korea. In order to systematical analysis of fare policy, such viewpoints as of fare decision method, the card fare discounting system, the structure of fare, and the government subsidy policy were reviewed. Based on the results from the review, various problems and improvement plans were drawn. Through this study, appropriate fare systems to urban railway users could be given to improve the chronic deficit problem of urban railway operators.

• 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".

• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.93-106
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• 2020

Trade is an important economic activity. In particular, since the establishment of the World Trade Organization (WTO), the scope of trade has been expanding due to events such as the entry of China into the WTO in 2001, the establishment of a multilateral trading system, mitigation and integration of trade barriers, and the establishment of the free trade agreement (FTA). Despite the expansion of the trade market, however, extreme events such as the 2008 global financial crisis, the 2016 Brexit, and the 2018 US-China trade war have had a direct negative impact on the trade market. Therefore, the present this study analyzed the dependence structure between the international shipping freight rate index, a variable representing trade activities, and the trade uncertainty between the US and China. The following is a summary of the analysis results. First, the US-Chinese trade policy uncertainty and international shipping freight rate index presented a Frank copula and rotated Clayton copula 270° distribution, respectively, showing the same distribution structure for each country. Second, the Kendall's tau correlation revealed a negative dependence between the international shipping freight rate index and US-Chinese trade policy uncertainty. The degree of dependence was greater in the combination of uncertainty in China's trade policy and international shipping freight rates. In other words, the dependence of global demand and trade policy uncertainty confirmed that China was stronger than the US. Finally, the tail dependence results revealed that the US-Chinese trade policy uncertainty and international shipping freight rates were independent of each other. This means that extreme events related to the trade policy uncertainty or international shipping rate index were not affected by each other.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Korea Trade Review
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• v.43 no.6
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• pp.51-70
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• 2018

This study is to define the relationship between capital structure and the market share in the global shipping market, estimating the debt-equity ratio. To analyze the impact of the debt-equity ratio on market share, this study collected data from the 100 largest shipping companies from 2010 to 2017. Results identified that global shipping lines moderate their debt-equity rates to 62%, and all of them strategically utilize debt in order to increase market share in global shipping market. In comparison between the group focused on cargo volume and another group focused on freight rates, it is found that the group focused on cargo volume increase their handling cargo volume through increasing the debt rates. Another group used debt rate for reducing the freight rate and enhancing market power. Furthermore, after classifying the samples into high-growth and low-growth companies, this study compared the group focused on cargo volume and another group focused on freight rates. As a result, the low-growth group showed more significant impacts of the debt rate on market share than the high-growth group. The results of this study provide useful insight for future strategic decision making of shipping lines in the global shipping market.

• Journal of Navigation and Port Research
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• v.33 no.6
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• pp.409-414
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• 2009

As Korea has the economic structure heavily depending on exporting commodities, the importance of both maritime and air transportation mode is significant. Accordingly, for the sustainable and stable export the freight transportation market by mode needs to be developed complementarity each other. This paper, therefore, aims at analysing the LCL commodities transported by the two mode in terms of unit price, freight charge bearability and price structure. For this around 500 bill of lading(B/L) are collected and analysed resulting in deriving the distinctive characteristics of commodities by mode and the implication for price policy.

• 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.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.181-190
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• 2018

The goal of this study is empirically to investigate the asymmetric relationship between two variables using the dry cargo freight rates and raw material price data from January 2012 to May 2018. First, we estimate the asymmetry of macroeconomic indicators of commodity prices by using a two - step threshold cointegration test. Second, the asymmetric relation test of the trade balance of existing commodity price changes is tested by bypassing to the high frequency dry cargo freight rate index. As a result of the estimation, in contrast to the existing linear analysis, each boundary value for the lower limit and the upper limit has different asymmetry. This implies that the period of fluctuation of the sudden residual that causes irregular rate of return fluctuations does not establish a long term equilibrium relationship between the raw material price and the dry cargo freight rate. Therefore, in order to consider the sudden price change in the analysis, it is necessary to include the band of inaction that controls the irregular volatility, which is consistent with the asymmetry hypothesis.

• Journal of Korean Society of Transportation
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• v.1 no.1
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• pp.64-81
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• 1983

소비자 행태에 관한 신고전 경제이론을 통행수요의 분석에 그대로 적용하는데 문제 가 되는 특기 사항으로는 운임이외에 통행시간과 같은 서어비스 질도 통행수단의 선택에 영 향을 미친다는 점과 특정 두지점간의 통행에서 동일한 소비자도 여러 수송수단을 이용한다 는 점을 들수 있다. 본 연구의 주제는 이와같은 통행수요의 독특한 특성을 반영할 수 있는 소비자의 효용최대화모형의 설정과 이 모형에서 유도된 수요함수의 구조를 분석함에 있다. 상술하면, 통행시간 가치의 불확실성 아래에서 효용을 극대화하는 의사결정문제를 Stochastic Programming 모형으로 표현하였다. 또한 이 모형에서 유도된 특성 통행수단의 수요함수는 이 수단이 가장 유리한 시간가치의 범위에 대한 부정적분으로 표현되며 이 적분 범위와 피적분함수는 모든 경쟁수단의 운임과 통행수단의 함수로 정의됨을 증명하였다. 또 한 수요함수는 통행수요에 관한 통계분석모형에서 묵시적으로 가정되고 있는 통행수단간 대 체성(Property of gross substitute)과 대각방향우세성(Property of diagonal dominance)등의 특성을 가지고 있음을 보여 주었다.

• Aerospace Industry
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• v.66
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• pp.26-29
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• 1999

미국의 항공산업은 바야흐로 백화재방 전성기에 있다. 이것은 항공기의 제조, 운항 등 전분야에 걸쳐있으나 처음부터 이렇게 백가쟁명형은 아니었다. 항공기 제조업계가 최근에 와서 냉전 체제의 종식에 따라 구조조정이 불가피하여 지금은 유수 몇개사로 집중되고 있다. 그러나 운항회사의 경우는 약육강식의 경향이 없는 것은 아니지만 약 20여년 전부터 언제나, 어디서나, 어떤 운임 체계로나 설립도 자유이고 폐업도 자유라는 완전 자유 풍토가 되어있다.