• KDI Journal of Economic Policy
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• v.35 no.4
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• pp.31-61
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• 2013

The purpose of this study is to calculate the economic value of transport demand forecasting risks in the road PPP project. Under the assumption that volatility of the road PPP project value occurs only in regard with uncertainty of traffic volume forecasting, this study calculates the economic value of the traffic forecasting risks in the case of the road PPP project. To that end, forecasted traffic volume is assumed to be a stochastic variable and to follow the Geometric Brownian motion as time passes. In particular, this study attempts to differentiate itself from existing studies that simply use an arbitrary assumption by presenting the application of different traffic volume growth volatility and the rates before and after the ramp-up period. Analysis of the case projects reveals that the risk premium related to traffic volume forecast of the project turns out as 7.39~8.30%, without considering option value-such as minimum revenue guarantee-while the project value volatility caused by transport demand forecasting risks is 17.11%. As the discount rate grows higher, the project value volatility tends to decrease and volatility in project value is always suggested to be larger than that in transport volume influenced by leverage effect due to fixed expenditure. The market value of transport demand forecasting risk-calculated using the project value volatility and risk premium-is analyzed to be between 0.42~0.50, implying that a 1% increase or decrease in the transport amount volatility would lead to a 0.42~0.50% increase or decrease in risk premium of the project.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.1-11
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• 2006

This study conducts a theoretical research on road traffic noise. Also, the domestic road noise forecast models were compared each other and analyzed with advanced countries' models to indicate the application possibility and problems. For the establishment of a general formula, we compared the forecasted value with the actual value applied in the formula proposed by the National Environment Institute, and examined the necessary improvement of the domestic road traffic noise forecast model. Also, a regression model was built to examine the relationship between traffic factors and noise. The traffic volume and speed are the main traffic factors used in this formula to affect the noise. From the results, it was found that the speed had a closer relationship with the noise rather than the traffic volume. Therefore, to decrease road noise, it is more important to control traffic speed. The spatial effect of road traffic noise within the apartment complexes was used in the case study to derive location-specific adjustment values. We surveyed the road traffic noise of three apartment complexes, and found that the road traffic noise within each complex was affected at plane level as well as at three-dimensionally. In other words, as the distance from the sound origin grows farther, noise level decreases. Also, it was found that noise increases as heigt goes up, but drops when the height goes beyond a certain level, and that the effect of noise decreases if there are obstacles along the path of the noise direction. Therefore, apartment site design should be done with consideration of the effects of noise in the future.

• Journal of Korea Port Economic Association
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• v.33 no.3
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• pp.17-34
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• 2017

This study aims to analyze the freight transport demand and benefit for the introduction of an automated freight transport system focusing on the Global Industry and Logistics City (GILC) in Busan. In pursuit of this aim, four alternatives were calculated - using the freight volume estimating methods and included, the number of businesses, the number of employees set up, future estimated cargo volume, and switched volume from other transport modes into the GILC. Economic benefits were analyzed against social benefits and costs accordingly. The result of the freight transport demand forecast found, the cargo volume of "Alternative 2-1" to be the most advantageous, applying the number of employee unit method and proportion of employees in Gangseo-gu, Busan. In addition to the conventional analysis of direct benefit items (reduction of transport time, traffic accidents and environmental costs), this study also considered additional benefit items (congestion costs savings, and road maintenance costs in terms of opportunity cost). It also considered advanced value for money research in guidance on rail appraisal of U.K, Federal Transport Infrastructure Plan 2003 of Germany, and RailDec of the United States. The study aims to further contribute to estimating minimum cargo transport demands and assess the economic feasibility of the introduction of new intermodal automated freight transport systems in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.295-303
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• 2017

In this paper, we analyze the trends of the international shipping market and the domestic and foreign factors of the crisis of the domestic shipping market, and identify the characteristics of the recovery of the Busan New Port trade volume which has decreased since the crisis of the domestic shipping market We quantitatively analyzed the future volume of Busan New Port and analyzed the trends of the prediction and recovery trends. As a result of analyzing Busan New Port container cargo volume by using big data analysis tool R, the variation of Busan New Cargo container cargo volume was estimated by ARIMA model (1,0,1) (1,0,1)[12] Estimation error, AICc and BIC were the most optimal ARIMA models. Therefore, we estimated the estimated value of Busan New Port trade for 36 months by using ARIMA (1, 0, 1)[12], which is the optimal model of Busan New Port trade, and estimated 13,157,184 TEU, 13,418,123 TEU, 13,539,884 TEU, and 4,526,406 TEU, respectively, indicating that it increased by about 2%, 2%, and 1%.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.61-70
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• 2010

The purpose of this study is to analyze the accuracy of traffic volume forecast by comparing an estimated to real traffic volume. For this study, total 10 sections of national highways, which are planned in 1980s and 1990s, were selected and traffic analysis data for highway construction were collected. In addition, targeted 10 sections were categorized into network-related and -unrelated sections. In the analysis of inaccuracy between the estimated and real traffic, for network-related sections, appeared to have lower inaccuracy. As time goes on after traffic open, inaccuracy between the estimated and real traffic appeared to be lower. In various section lengths, the longer the section length, the higher the inaccuracy is. Using 3 years passed data after traffic open, national highway have lower inaccuracy than expressway. However, the traffic analysis according to traffic open time resulted in little change of the inaccuracy.

• Journal of the Korean GEO-environmental Society
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• v.9 no.5
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• pp.21-27
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• 2008

The Sediment deposits in rivers and reservoirs are major components interfering with the useful function of the reservoirs, and clogging the inlet port at water intakes in rivers and erosion of pump impellers. Therefore, an accurate estimation method of sediment deposition is requisite to the efficient water resources investigation, planning and management. The objective of this paper is to forecast of reservoir sediment deposition using two dimensional model (SMS) to UnMun reservoir in GyeongSangBukDo. The RUSLE model showed that reservoirs volume was decreased $2,084.09{\times}10^6m^3$ after 50 years and $2,196.65{\times}10^6m^3$ after 100 years, which is plan flood level elevation (EL.152.12 m) reservoir. The two dimensional model showed that reservoirs volume was decreased $2,227.41{\times}10^6m^3$ after 50 years and $2,121.47{\times}10^6m^3$ after 100 years, which is plan flood level elevation (EL.152.12 m) reservoir. The results of this application showed that the use of two dimensional model was very effective for the estimation sediment deposits throughout the reservoir.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.12
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• pp.802-807
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• 2008

A Study on Typical Rates of Water-use for School Facilities has been carried out in this work. Water supply system is given much weight in school facilities. Therefore, it set up a basis efficiency using of water sources to calculate typical rates of water use. The results are summarized as follows; 1) On the whole, typical rates of water-use was founded out 15 L/stu. d in pirmary school, 10 L/stu. d in middle school and 30 L/stu. d in high school smaller than the existing it. It was rate of water-use change as season and Max. Rates of water-use was July. 2) I deem that school hours are 5 hour's in primary school, 7 hour's in middle school and 8 hour's in high school. It the concept of 1 hour that is lesson time 40 minutes and resting time 10 minutes in primary school, lesson time 45 minutes and resting time 10 minutes in middle school and lesson time 50 minutes and resting time 10 minutes in high school. 3) It is desired that we calculate the volume of pump and water tank throughout this concept and the size of water tank should be 1.5 times with taking peak load into consideration by this study on typical rate of water-use. 4) The amount of using water increases in gradually and I consider the life cycle of facilities is more than 10 years. As a result, I can forecast that the size will be insufficiency but I deem that if we devise a plan about parallel pumping on water tank space, we can cope with it. Also, it is expected that we can cut back the transport energy by controlling pump volume.