• Journal of Distribution Science
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• v.11 no.8
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• pp.5-14
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• 2013

Purpose - We live in a world of constant change and competition. Many airports have specific competitiveness goals and strategies for achieving and maintaining them. The global economic recession, financial crises, and rising oil prices have resulted in an increasingly important role for facility investment and renewal and the implementation of appropriate policies in ensuring the competitive advantage for airports. It is thus important to analyze the factors that enhance efficiency and productivity for an airport. This study aims to determine the efficiency levels of 20 major airports in East Asia, Europe, and North America. Further, this study also suggests suitable policies and strategies for their development. Research design, data, and methodology - This paper employs the DEA-CCR, DEA-BCC, and DEA-Malmquist production index analysis models to determine airport efficiency. The study uses data on the efficiency and productivity of the world's leading airports between 2006 and 2010. The input variables include the airport size, the number of runways, the size of passenger terminals, and the size of cargo terminals. The output variables include the annual number of passengers and the annual cargo volume. The study uses basic data from the 2010 World Airport Traffic Report (ACI). The world's top 20 airports (as rated by the ACI report) are investigated. The study uses the expanded DEA Model and the Super Efficiency Model to identify the most effective airports among the top 20. The Malmquist productivity index analysis is used to measure airport effectiveness. Results - This study analyzes longitudinal and cross-sectional data on the world's top 20 airports covering 2006 to 2010. A CCR analysis shows that the most efficient airports in 2010 were Gatwick Airport (LGW), Zurich Airport (ZRH), Vienna Airport (VIE), Leonardo da Vinci Fiumicino Airport (FCO), Los Angeles International Airport (LAX), Seattle-Tacoma Airport (SEA), San Francisco Airport (SFO), HongKong Airport (HKG), Beijing Capital International Airport (PEK), and Shanghai Pudong Airport (PVG). We find that changes in airport productivity are affected more by technical factors than by airport efficiency. Conclusions - Based on the study results, we offer four airport development proposals. First, a benchmark airport needs to be identified. Second, inefficiency must be reduced and high-cost factors need to be managed. Third, airport operations should be enhanced through technical innovation. Finally, scientific demand forecasting and facility preparation must become the focus of attention. This paper has some limitations. Because the Malmquist productivity index is based on the hypothesis of the, the identified production change could be over- or under-estimated. Further, as DEA estimates the relative efficiency. It also cannot generalize to include all airport conditions because the variables are limited. To measure airport productivity more accurately, other input variables and environmental variables such as financial and policy factors should be included.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.83-94
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• 2022

The number of public rental housing has increased according to the government's 「Housing Welfare Roadmap (2017)」, and facility maintenance costs for the demand of improvement of performance and residential standards due to the aging of long-term public housing are significantly increasing. Consequently, the financial burden of public housing rental business for maintaining stocked housing is aggravated. However, there is a lack of objective data to analyze the size of the maintenance costs that are executed by the type of repair work, and the elapsed years of the aged long-term public rental housing. This study analyzes the execution status of 33 long-term public rental housing complexes located in Seoul for 14 to 28 years of elapsed years based on the data of maintenance costs. In addition, this study proposes a model to predict the maintenance costs by elapsed years by dividing 'Long-term Repair Plan Work and Government-Funded Project [Y₁]', 'Planned Repair Work and General & Unplanned Repair Work [Y₂]', and 'Total maintenance costs [Y₃]'. It is intended to be used as basic data for the establishment of the maintenance plan at the stage of setting up the budget and the establishment of the sustainable operation plan for public rental housing

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.12-20
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• 2015

Construction industry has played a pivotal role in the national economy, but the crisis situation of a construction industry has been worse due to the lack of recognition of the contribution of a construction industry. In particular, the transport sector is responsible for a critical function in the movement of humans and material resources, and has a profound impact on national competitiveness and the peoples' welfare, which requires quantitative analysis. In this study, economic contribution and impact of the transportation sector are measured based on the input-output model. Road and railway facilities account for 1.03% and 0.165% of the total industry respectively, and consist of a final demand and total output. Although value-added inducing effect is small, production inducing effect and backward linkage effect has been high. The results in this study will be used as the basic information for validity of investment and policy decisions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.76-91
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• 2017

The Area of a domestic railway station is designed based on the 4-step traffic demand forecasting model with the average daily passenger count as one of its parameter. However, nowadays, due to increasing rate of railway station's function, the non-passengers are increasing. In order to consider those non-passengers who aren't using trains, assumed volume are added to the average daily passenger count of station to estimate the area, but the criteria being applied has no concrete basis. Therefore, this study aimed to recalculate the increasing non-passenger rate based on actual survey data of station users in any type of railway station to obtain the optimum area. Subsequently, the the design area was performed through pedestrian simulation. According to the result of the simulation, it was found that the total space of the exciting railway stations can be reduced up to 45% and will still satisfy the level of service(LOS) requirement.

• The Journal of Sustainable Design and Educational Environment Research
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• v.15 no.1
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• pp.30-39
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• 2016

Students receiving plan is not based on short-term indicators, such as student-centered, student-induced factor to address school needs new complaint, it is necessary to establish the school in the center of a long-term (30 years) perspective. Therefore, analysis of Cheongju students can examine the entire 30 years of the elementary school in this study are as follows: First, given the increasing number of students in seven models and presented the case to its types. Second, considering the geographical characteristics and the development of regional characteristics classify 55 elementary Schools in Cheongju City by dividing the number of students increase or decrease trend to 10 zones the results are as follows: Students Number increasing school group of 4 schools, 15 schools students Number fell in shot Term, the Students Number dropped in middle Term 26 schools, 10 was a small school. In particular, it is urgently necessary to establish measures for these small schools. Third, despite the reduced number of students indicated in the analysis result, caused the social conflict factors by excessive new school requirements. It also caused a number of students from schools when the Curve of Students Number are to remain flat or decline. It shows that no additional new demand of School in the region. Fourth, the number of students increasing trend forecasting model

• The Journal of Natural Sciences
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• v.10 no.1
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• pp.199-208
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• 1998

This study forecasts the trend of ethnic theme through market survey, concentrating on Los Angeles market. First, the background of ethnic theme was examined, and the present situation of shops, department sores, and headquarter was also surveyed. After that, fashion trend suitable for market was suggested by analyzing the life style of consumers through zip code. The results of the study are as follows. The conspicuous trend of '97 F/W retail stores is ethnic. This reaction to complicated modern life, and symbolizes the desirable evaluation on the simpleness of basic life and nature. The model of ethnic design is identified in natural clothing, primitive arts, ethnic culture and African theme. In short, this ethnic fashion is expressed as simpleness, naturalism convenience and freedom. On the other hand, the standard of general department stores such as Broadway and Robinson May which are the headquarter of this trend is to satisfy various consumers with various styles. Ethnic goods from Broadway has not arrived at the top for its introducing step. To elevate sales of these goods, promotion through VMD and suggesting various ethnic goods should be done. Besides, when analyzing the consumers of Beverly center Broadway, the target of these goods are mostly professional young people in their 25-34 and 35-44. The life style of these people emphasizes sophisticated life in aspects such as job-oriented activities, and up-to-date fashion. Especially, image is very important. They want individuality different from others. These images are diversified from simpleness, naiveness to sexy character. Accordingly, suggesting fashion trend satisfying the demand of consumers through market survey will make fashion market create infinite possibilities.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.89-108
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• 2006

Korea has been entering the ageing society as the population of age over 65 shared over 7% since the year 2000. The ageing society needs to have transportation facility considering elderly people's travel behavior. This study aims to understand the elderly people's travel behavior using recent data in Korea. The activity schedule approach begins with travel outcomes are part of an activitv scheduling decision. For tho?e approach. used discrete choice models (especially. Nested Logit Model) to address the basic modeling problem capturing decision interaction among the many choice dimensions of the immense activity schedule choice set The day activity schedule is viewed as a sot of tours and at-home activity episodes tied togather with overarching day activity pattern using the Seoul Metropolitan Area Transportation Survey data, which was conducted in June, 2002. Decisions about a specific tour in the schedule are conditioned by the choice of day activity pattern. The day activity scheduling model estimated in this study consists of tours interrelated in a day activity pattern. The day activity pattern model represents the basic decision of activity participation and priorities and places each activity in a configuration of tours and at-home episodes. Each pattern alternative is defined by the primary activity of the day, whether the primary activity occurs at home or away, and the type of tour for the primary activity. In travel mode choice of the elderly and non-workers, especially, travel cost was found to be important in understanding interpersonal variations in mode choice behavior though, travel time was found to be less important factor in choosing travel mode. In addition, although, generally, the elderly was likely to choose transit mode, private mode was preferred for the elderly over 75 years old owing to weakened physical health for such things as going up and down of stairs. Therefore. as entering the ageing society, transit mode should be invested heavily in transportation facility Planning tor improving elderly transportation service. Although the model has not yet been validated in before-and-after prediction studies. this study gives strong evidence of its behavioral soundness, current practicality. and potential for improving reliability of transportation Projects superior to those of the best existing systems in Korea.