• Review of Culture and Economy
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• v.17 no.2
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• pp.3-28
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• 2014

The characteristics of performing arts differ from those of utilitarian goods in terms of economics. Factors other than price need to be considered to understand the demand for performing arts. Audience surveys as well as econometric demand studies have confirmed that socio-economic factors such as age, income, employment, and education are major determinants of the demand for performing arts. This study focused on the attributes of concerts rather than consumer characteristics to determine the concerts audiences select in terms of transaction cost. Genre, price, internet search trends, and the purpose of performance as well as price are tested as determinants of demand by using the data set for a major concert hall in Seoul. Genre and the specific purpose of concerts influence the demand for concerts. Internet search trends of the performer are used as indicators of popularity and information exposure, which are positively correlated with demand. This result supports the hypothesis that larger audiences would attend concerts that require lower information search costs. To note, price has a positive effect on demand in the higher price range, which means that concerts at higher prices attract larger audiences, whereas normal goods have a negative slope in the demand curve. This result can be explained by the hypothesis that consumers use price as an indicator of the quality expected of a concert. Transaction cost for selecting classical concerts thus forms an inverse-U shape curve against ticket price. These results provide some explanation of why audiences of classical music choose to attend concerts at high ticket prices while offering evidence in favor of the hypothesis that performing arts are selected in a social context.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• KDI Journal of Economic Policy
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• v.9 no.1
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• pp.43-67
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• 1987

산업구조(産業構造)의 고도화과정(高度化過程)에서는 고용흡수력(雇傭吸收力)이 감소(減少)하는 양적측면(量的側面)의 과제(課題)와 기술인력(技術人力)의 수요(需要)가 상대적으로 증가(增加)하는 질적측면(質的側面)의 인력정책과제(人力政策課題)가 동시에 야기(惹起)된다. 본(本) 논문(論文)에서는 이러한 여건변화(與件變化)에 대응(對應)하는 정책과제(政策課題)를 도출(導出)하기 위하여 인력수요(人力需要)의 결정요인(決定要因)을 실증분석(實證分析)하였다. 거시분석(巨視分析)에서는 1970~84년(年) 기간(期間)의 연간(年間) 시계열자료(時系列資料)를 이용(利用)하여 노동수요함수(勞動需要函數)를 추정(推定)하여 산업별(産業別) 결정요인(決定要因)을 비교분석(比較分析)하였으며, 미시분석(微視分析)에서는 섬유(纖維) 자동차(自動車) 전자산업(電子産業)에서 임의추출(任意抽出)한 200개(個) 표본기업(標本企業)에 대한 횡단면자료(橫斷面資料)를 이용(利用)하여 기업(企業)의 인력수요행태(人力需要行態)를 분석(分析)하였다. 산업별(産業別) 노동수요(勞動需要) 분석결과(分析結果)를 보면, 자본비용(資本費用)에 대한 노동비용(勞動費用)의 상대적상승(相對的上昇)이 노동수요(勞動需要)를 감소(減少)시켜 왔으나, 고용(雇傭)은 기본적으로 자본투자(資本投資)로부터 더 큰 영향을 받는 것으로 나타났으며, 또한 자본(資本)의 고용창출효과가(雇傭創出效果), 특히 제조업부문(製造業部門)에서, 시간(時間)의 경과(經過)에 따라 감소(減少)하는 것으로 분석(分析)되었다. 미시분석결과(微視分析結果)를 보면, 전문대졸(專門大卒) 및 고졸학력(高卒學力) 근로자(勤勞者)에 대한 수요(需要)는 중소기업(中小企業)에서, 대졸(大卒) 및 중졸이하학력(中卒以下學歷) 근로자(勤勞者)는 대기업(大企業)에서 흡수(吸收)하려는 것으로 나타났으며, 생산요소중(生産要素中) 노동(勞動)에 대한 투자우선순위(投資優先順位)는 자동차산업(自動車産業)에서 높게 나타났으며, 숙련기능인격(熟練技能人格)에 대한 수요(需要)는 세 산업(産業) 모두 높게 나타났다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1488-1494
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• 2011

In this paper, we propose an algorithm using linear regression model that forecasts the demand of heated water in winter. To supply heated water to apartments, stores and office buildings, Korea District Heating Corp.(KDHC) operates boilers including electric power generators. In order to operate facilities generating heated water economically, it is essential to forecast daily demand of heated water with accuracy. Analysis of history data of Kangnam Branch of KDHC in 2006 and 2007 reveals that heated water supply on previous day as well as temperature are the most important factors to forecast the daily demand of heated water. When calculated by the proposed regression model, mean absolute percentage error for the demand of heated water in winter of the year 2006 through 2009 does not exceed 3.87%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.133-139
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• 2011

We built subway stops in order to meet demand. To do so, a standardized method is used to predict the demand. However, in some subway stops there are only few people moving around sparsely, but in some other stops there are too many people crammed in a crowd. The gap between forecasting and actual uses varies from 10% to more than 1,000%. This study is aimed to find out where this discrepancy between predicted vs. actual demand for urban rail transit comes from. Specifically, 40 subway stops in Seoul Metropolitan Area, which were opened last 10 years, are examined. This study suggests that, for better forecasting, we need to consider stops' locational characteristics as well as weekday commute-oriented exogenous factors. Locational characteristics includes; whether a stops is a terminal and/or weekend tourism node. There seems no "one size fits all" solution for transit demand forecasting; locational characteristics need to be reflected.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6860-6868
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• 2015

In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.107-116
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• 2010

The vehicle travel time between locations in a downtown is greatly influenced by both complex road conditions and traffic situation that changes real time according to various external variables. The customer's demands also stochastically change by time period. Most vehicle routing problems suggest a vehicle route considering travel distance, average vehicle speed, and deterministic demand; however, they do not consider the dynamic external environment, including items such as traffic conditions and stochastic demand. A realistic vehicle routing problem which considers traffic (smooth, delaying, and stagnating) and stochastic demands is suggested in this study. A mathematical programming model and hybrid genetic algorithm are suggested to minimize the total travel time. By comparing the results considering traffic and stochastic demands, the suggested algorithm gives a better solution than existing algorithms.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.31-40
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• 2009

Some road charges toll to finance the cost or to manage traffic congestion. With a growth of PPI projects, toll roads would be increase continuously. Tolls have a considerable influence on user's route choice, and sometimes can affect to the departure time and even to mode choice. For modelling toll roads, user's WTP or VOT has an important role and it is general that VOT is equivalent to the wages of workers. The current way of modelling technique yields various toll price elasticity from low to high. When there exist few alternative routes, unrealistic result that all traffic assigned to some shortest path may occur. The toll price elasticity can be influenced by alternative route and congestion level, but some result shows nearly unrealistic patterns. The model to forecast more realistic toll road demand is very essential for estimating toll revenue, choice of optimal toll level & collecting location and establishing toll charge strategy. This paper reviewed some literatures about toll road modelling and tested case study about the assignment technique with different VOT. The case study shows that using different VOT yields more realistic result than the use of single VOT.

• Journal of the Society of Disaster Information
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• v.10 no.1
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• pp.49-60
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• 2014

For the demand analysis of Metropolitan Train Express project, the catchment area of station should be reevaluated considering the journey speed of it. In this paper, we estimated travel mode choice model using stated preference data including Seoul metropolitan express train and compared the parameters of access/egress travel time between existing metro and Seoul metropolitan express train. The parameter of Seoul metropolitan express train is 2.5 times smaller than that of existing metro. Consequently, the catchment area can be expanded in same proportion. It can be concluded that the result of demand forecasting can be increased by 10% accommodating the expanded catchment area.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.29-30
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• 2007

본 논문은 특수일 전력 수요 예측을 위해 온도 효과를 고려한 데이터 추출법을 이용하여 특수일 전력 수용 예측 오차율을 감소시키는 방법을 제시한다. 제안된 기법의 타당성을 확인하기 위해 논문에서는 통계학에서 사용되는 결정계수를 이용한다. 결정계수를 이용하여 온도효과의 고려 여부가 오차율에 미치는 영향을 분석하였다. 또한 제안된 기법은 1996년 특수일 오차율을 기존 논문의 결과와 비교 분석하여 기존 방식 대비 특수일 전력 수요예측 관련 우수성을 보였으며, 최근 데이터인 2006년 특수일 전력 수요 예측을 통하여 검증하였다.