• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.19-28
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• 2009

In case of land development projects constructed, to solve induced transportation volume needs analysis of traffic demand. Trip-generation of land development projects is exactly predicted by using traffic instigating-basic-unit in each facility of land developments. But in case of a phase of trip-distribution, because a range of destinations is very enormous and it needs enormous data to reflect all of its characters, whenever trip-distribution is predicted, the method which assumes the rate of trip-distribution is same both before completion of land development projects and after is often used. But because there is no exact criterion, the method suggested above is also affected by subjective opinion. Accordingly, this study look over using trip-distribution of specific areas's DB and suggests a size of zone to predict a distribution of land development projects exactly. Also production - constrained gravity model which uses the gap between a distribution of suggested ranges and induced land development project is suggested for more exact prediction of trip-distribution. Besides accuracy of prediction is scrutinized by using Mean Squared Error.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.33-44
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• 2013

Personal Rapid Transit(PRT) has emerged as a promising transportation mode for transit-oriented sustainable communities. In this study, an alternative design of questionnaire survey is proposed in order to capture traveler's perception of an unexperienced transportation mode. This study aims at predicting the mode choice diversion behavior of potential PRT users who do not have experience of using it previously, considering their willingness-to-pay. The proposed model was applied to predict an aggregate forecast of PRT patronage for the city of Songdo where PRT is considered to be constructed. For validation of the proposed model, the price elasticity of PRT demand was analyzed, compared with existing models. The analysis results suggest that the proposed design of questionnaire survey is able to capture respondents' attitude and perception to unexperienced transportation mode in an effective manner. Also, they show that the proposed diversion rate model is more realistic than existing models in explaining the effects of users' willingness-to-pay for predicting PRT patronage.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.294-294
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• 2019

The system for predicting flood of river at Flood Control Office is made up of a rainfall-runoff model and FLDWAV model. This system is mainly operating to predict the excess of the flood watch or warning level at flood forecast points. As the demand for information of the management and operation of riverside, which is being used as a waterfront area such as parks, camping sites, and bike paths, high-level forecasts of watch and warning at certain points are required as well as production of lowland flood forecast information that is used as a waterfront within the river. In this study, a technology to produce flood forecast information in lowland areas of the river used as a waterfront was developed. Based on the results of the 1D hydraulic analysis, a model for performing spatial operations based on high resolution grid was constructed. A model was constructed for Andong district, and the inundation conditions and level were analyzed through a virtual outflow scenarios of Andong and Imha Dam.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.531-543
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• 2014

Research on activity-based travel demand forecasting and activity-travel simulator has received an international attention for the last two decades. Ways to develop the activity-based simulator may be manifold. It is obvious that importing an existing simulator that has been proven internationally likely reduces the development cost and the risk of failure. By definition of the activity-based approach, however, the details of an activity-based simulator inevitably relies on particular social, economic and cultural characteristics of the society where the simulator is developed. When importing such a simulator from overseas, the researcher should be aware of the importance of tuning the system for the society to which the imported system is applied. There are many potential works on this, including for example the tuning of data structure that is likely different form of the original system. The authors are yet aware of certain research on those. The current paper aims to report the result of transforming the input data for applying the existing activity-travel simulator to Seoul. The paper first introduces FEATHERS that was developed in Belgium having Albatross which is the core of system. FEATHERS Seoul that is under development and modified version of the original FEATHERS is briefly described and the related problems are discussed. The paper then explored to resolve and to alleviate such problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.477-483
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• 2010

The existing mass transport system, with its limited capacity and the saturated road networks, has given cause for a new means of transport to be developed, and strong demands for such new means of transport are observed more than ever. However, the traffic authority is seeking a new transport system that focuses more on LRT(Light Rail Transit), a downsized version of the existing urban railroad, rather than one that is appropriate to solve the traffic problems. Moreover, local governments are experiencing difficulties in planning their own mass transportation(bus or urban railroad) as they have no specified criteria for selecting a mass transport system. Accordingly, there has been an increasingly loud voice that calls for criteria to determine which mass transport system befits each transportation corridor. This paper develops a mass-transport demand forecasting model based on the GIS Buffering analysis of each transportation corridor in the city, sets up the capacity for each mass transport system and presents the criteria for selecting an optimal mass transport system for each transportation corridor. It also presents a methodology that identifies necessary and sufficient conditions for selection and evaluation, since it is most important to select the optimal mass-transport system that can meet the demand by each mass-transportation corridor.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.544-553
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• 2014

Activity-based models analyze individuals' various daily activities that are identified as a decision-making unit for transportation planning. In other words, it is the model that determines the types of activities according to the social, economic and situational characteristics of the groups with the same activity patterns and predicts individuals' activity time, distance, spatial movement and transportation mode. The activity-based model is a method of estimating more efficient and realistic demand in transportation forecasting because traffic is regarded as a complex decision-making process that an individual and other people participate in. In this paper, we grasp the factors affecting choice behavior of activity pattern and analyze choice behavior of activity pattern based on multi-dimensional characteristic of each person. First, we classify activity types of reviewing the trip chain and activity purpose. Next, we identified preferable activity types using complicated characteristics of main agent of activity. We concluded that choice behavior of activity pattern is dependent on complex characteristics of each agent, and further multi-dimensional characteristics of each person are affected over the whole decision process of activity schedule.

• Journal of Energy Engineering
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• v.23 no.2
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• pp.83-92
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• 2014

This paper attempts to conduct a comparative analysis on the economic effects of integrated-energy and manufactured gas supply sectors. To this end, an input-output (I-O) analysis is applied using most recently published 2011 I-O table. In particular, the two sectors are specified as exogeneous to identify the economic effects on own and other sectors. Production-inducing effect, value-added creation effect, and employment-inducing effect are quantified based on demand-driven model. Supply shortage effect and price pervasive effect are analyzed employing supply-driven model and Leontief price model, respectively. The results show that production-inducing effect, value-added creation effect, and employment-inducing effect of integrated-energy and manufactured gas supply sectors are estimated to be 1.5461 vs. 1.0297, 0.4759 vs. 0.1941, and 2.2885 vs. 0.4053 respectively. Price pervasive effects of the 10% increase in integrated-energy and manufactured gas supply sectors are computed to be 0.0127% and 0.1585%, respectively. This information can be utilized in forecasting the economic effects of introducing integrated-energy or manufactured gas as a heating source and the impacts of a rise in price of integrated-energy or manufactured gas on price level of other sectors.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.75-84
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• 2023

Many developing countries face challenges in estimating long-term discharge due to the lack of hydrological data for water supply planning, making it difficult to establish a rational water supply plan for decision-making on water distribution. The study area, the Bandung region in Indonesia, is experiencing rapid urbanization and population concentration, leading to a severe shortage of freshwater. The absence of water reservoir prediction methods has resulted in a water supply rate of approximately 20%. In this study, we aimed to propose an approach for predicting water reservoirs in developing countries by analyzing water safety and potential water supply using the MODSIM (Modified SIMYLD) network model. To assess the suitability of the MODSIM model, we applied the unit hydrograph method to calculate long-term discharge based on 19 years of discharge data (2002-2020) from the Pataruman observation station. The analysis confirmed alignment with the existing monthly optimal operation curve. The analysis of power plant capacity revealed a difference of approximately 0.30% to 0.50%, and the water intake safety at the Pataruman point showed 1.64% for Q95% flow and 0.47% for Q355 flow higher. Operational efficiency, compared to the existing reservoir optimal operation curve, was measured at around 1%, confirming the potential of using the MODSIM network model for water supply evaluation and the need for water supply facilities.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.