• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.59-66
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• 2020

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.61-83
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• 2009

This study developed a variable demand traffic assignment model by stable dynamics. Stable dynamics, suggested by Nesterov and do Palma[19], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with the user equilibrium model, which is based on the arc travel time function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on congestion. It is therefore expected to be a useful analysis tool for transportation planners. In this study, we generalize the stable dynamics into the model with variable demands. We suggest a three stage optimization model. In the first stage, we introduce critical travel times and dummy links and determine variable demands and link flows by applying an optimization problem to an extended network with the dummy links. Then we determine link travel times and path flows in the following stages. We present a numerical example of the application of the model to a given network.

• Journal of Korean Society of Transportation
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• v.19 no.1
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• pp.89-99
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• 2001

The conventional studies on equilibrium network design problem(ENDP) with fixed travel demand models assume that the future OD travel demand might not be changed even if the structure and the capacity of the network are improved. But this fixed demand assumption may loose its validity in the long-range network design because OD travel demand actually shifts with the network service level. Thus, it is desirable to involve the variable travel demand which is determined endogenously in the model in the optimal network design. In this paper a hi-level model formulation and solution procedure for ENDP with variable travel demand are presented. Firstly It is considered how to measure the net user benefits to be derived from the improved in link capacities, and the equilibrium network design problem considered here is to maximize the increase of net user benefit which results from a set of lift capacity enhancements within the budget constraints, while the OD travel demands and link travel times are obtained by solving the lower level network equilibrium problem with variable demand. And secondly sensitivity analysis is carried out to find the links to which the network equilibrium flow pattern is the most sensitive. Finally numerical example with simple network is carried out to test the validity of the model.

• Journal of Agricultural Extension & Community Development
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• v.30 no.1
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• pp.43-52
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• 2023

This study aims to examine the effects of GDP as a proxy variable of income, consumer price index as a proxy variable of price, and foot-and-mouth disease and avian influenza as derby variables on rural tourism demand. The independent variables in this research were gross domestic product(GDP), consumer price index(CPI), and dummy variable(DM) such as food & mouth disease & highly pathogenic avian influenza. Results showed that GDP affected tourism demand positively whereas DM influenced negatively. The study suggested that it was important for policy-mconsider GDP and DM when making decision on strategic tourism management. In conclusion, first, gross domestic product was found to have a statistically significant effect on rural tourism demand. Second, avian influenza was found to have a statistically negative effect on rural tourism demand. The results of this study can be used to establish a reasonable rural tourism policy in the future economic dimension.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.155-167
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• 2008

This paper studies a multiple user class variable demand user equilibrium and system optimal condition, and then establishes solution algorithms for them. The traffic network equilibrium is accomplished with basis on following assumptions. For considering heterogeneous road user, several user classes have discrete set of VOTs and the travel demand of each user classes varies according to generalized travel cost. this paper specifically investigates following question on multi-class variable demand: Are user equilibrium flows pattern dependent on the unit (time or money) perceived by road user classes? What is system optimal condition according to the unit used in measuring the travel cost or disutility? Finally, using this network equilibrium condition, The traffic assignment algorithm of each equilibrium condition are established.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Journal of Korean Society of Forest Science
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• v.95 no.2
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• pp.203-208
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• 2006

This study compared the roundwood demand prediction accuracy of econometric and time-series models using Korean data. The roundwood was divided into softwood and hardwood by species. The econometric model of roundwood demand was specified with four explanatory variables; own price, substitute price, gross domestic product, dummy. The time-series model was specified with lagged endogenous variable. The dummy variable reflected the abrupt decrease in roundwood demand in the late 1990's in the case of softwood roundwood, and the boom of plywood export in the late 1970's in the case of hardwood roundwood. On the other hand, the prediction accuracy was estimated on the basis of Residual Mean Square Errors(RMSE). The results showed that the softwood roundwood demand prediction can be performed more accurately by econometric model than by time-series model. However, the hardwood roundwood demand prediction accuracy was similar in the case of using econometric and time-series model.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.253-259
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• 2013

Investment scenarios in the transportation network design problem usually contain installation or expansion of multi-mode transportation links. When one applies the mode choice analysis and traffic assignment sequentially for each investment scenario, it is possible that the travel impedance used in the mode choice analysis is different from the user equilibrium cost of the traffic assignment step. Therefore, to estimate the travel impedance and mode choice accurately, one needs to develop a combined model for the mode choice and traffic assignment. In this paper, we derive the inverse demand and the excess demand functions for the multi-mode multinomial logit mode choice function and develop a combined model for the multi-mode variable demand traffic assignment problem. Using data from the regional O/D and network data provided by the KTDB, we compared the performance of the partial linearization algorithm with the Frank-Wolfe algorithm applied to the excess demand model and with the sequential heuristic procedures.

• Fire Science and Engineering
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• v.22 no.2
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• pp.9-19
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• 2008

Recently the systematic change in the fire and disaster prevention administration, which was less studied than other administrative systems, has been in progress. In particular, the fire service demands are increasing and forms are diversified. The first thing you must do is to make up the fire service power according to the fire service demands. Such an allocation results in social welfare satisfaction. The purpose of this study is to investigate the determinants of fire service force. And this study investigates that fire service demand is affected by socioeconomic factor. To do this, this study reviews fire service force, the fire service demand, socioeconomic factor and financial factor. And this study sets up four hypotheses based on the theoretical backgrounds and the past research. The statistical method used for the verification of hypotheses are multiple regression analysis and structural equitation analysis. The analysis showed that fire service demand and financial factor were positive significant variable for fire service power. But socioeconomic factor was a negative significant variable. Also the analysis showed that social factor was a positive significant variable and economic factor was a negative significant variable for fire service demand.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.37-44
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• 2016

The demand for electricity has a considerable impact on various energy sectors since electricity is generated from various energy sources. This paper attempts to estimate the electricity demand function and obtain some quantitative information on price and income elasticities of the demand. To this end, we apply a lagged dependent variable model to derive long-run as well as short-run elasticities using the time-series data over the period 1991-2014. Our dependent variable is annual electricity demand. The independent variables include constant term, real price of electricity, and real gross domestic product. The results show that the short-run price and income elasticities of the electricity demand are estimated to be -0.142 and 0.866, respectively. They are statistically significant at the 5% level. That is, the electricity demand is in-elastic with respect to price and income changes in the short-run. The long-run price and income elasticities of the electricity demand are calculated to be -0.210 and 1.287, respectively, which are also statistically meaningful at the 5% level. The electricity demand is still in-elastic with regard to price change in the long-run. However, the electricity demand is elastic regarding income change in the long-run. Therefore, this indicates that the effect of demand-side management policy through price-control is restrictive in both the short- and long-run. The growth in electricity demand following income growth is expected to be more remarkable in the long-run than in the short-run.