• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.437-444
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• 2012

This study features an analysis of the socio-economic factors of ten cities on the Honam-line that affect the number of train passengers. The 3 main factors based on the principal component analysis were the population, the distance between two regions, and the area size of each region while the number of automobiles has been conventionally used instead of the area size of each region. A formula to predict the train passengers by the regression analysis was developed and showed a good agreement to the number of real passengers. When Honam highspeed railway is opened, the traveling time between two regions as well as the area size of each regions should be more precisely considered.

• Journal of Korean Society of Rural Planning
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• v.24 no.1
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• pp.47-60
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• 2018

계획적이고 안정적인 영농을 위해 영농작업 인력을 확보하는 것은 매우 중요하다. 하지만 농가인구의 감소로 인한 농업인력 확보의 어려움과 농업노동 임금의 지속적인 증가는 경영주에게 이중의 고통이 되고 있다. 따라서 본 연구에서는 농가처분가능소득이 전국에서 가장 높은 강원도를 지역 표집으로 선정하여 Bivariate Probit 모형을 이용해 내국인과 외국인 고용의 상호 관계를 고려한 고용인력 수요 결정요인을 살펴보았다. 분석결과, 첫째, 3개월 이상 노동력을 고용하는 농가들의 경우 내국인 고용수요와 외국인 고용수요 간에는 양(+)의 상관관계가 있는 것으로 나타났다. 둘째, 전형적인 도시근교 농업의 특징을 나타내는 춘천시에 비해 강원도내 타 지역 농가들은 다른 변수들이 일정할 경우 내국인과 외국인의 고용수요가 증가하는 것으로 나타났다. 셋째, 젊은 경영주일수록 내국인 상시 고용에 대한 수요가 높고, 농가조직에 참여하고 있는 농가일수록 자가노동 확률은 0.13% 감소하고, 상시 농업 노동력에 대한 수요가 증가하는 것으로 나타났다. 이러한 분석결과를 종합한 농업 노동력 확보를 위한 정책적 시사점으로는 첫째, 상시고용된 농업인력들을 대상으로 국내외 문화 차이를 인지하고, 내국인 노동자와 외국인 노동자간의 협력 네트워크 구축을 위한 영농교육 확대가 필요하다. 둘째, 각 지역별로 내국인 또는 외국인 노동자의 상시고용 수요가 상이한 것으로 나타나 강원도내 지역별 농산업 현황을 기반으로 "(가칭)강원도 농업 인력 수급 플랫폼" 구축이 필요하다. 셋째, 청년창업농과 농가조직 참여 농가들을 대상으로 농작업 상시고용 인력을 우선적으로 매칭해 주는 것이 필요하다.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1581-1589
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• 2013

The aim of this study is to suggest a methodology for calculating the peak-hour ratio of passengers at urban railway stations by reflecting the characteristics of passenger demand patterns and the land use inventory of stations. To achieve this, urban railway stations in Seoul are divided into three groups by using factor analysis and cluster analysis. For each station group, we calculate five and four variables related to the passenger demand patterns and the land use inventory of stations, respectively, as well as the peak-hour ratios of passengers. Among these nine variables, average daily passengers and the location quotient (LQ) index for business services are selected as the classification criteria for station groups based on statistical tests. Using the two variables, a group allocation process is suggested to estimate the peak-hour ratio of passengers for a newly-constructed station. Evaluation results based on thirteen stations show that the proposed methodology produces lower errors than the currently-used guideline does. The results of this study contribute to establishing efficiently construction and operation plans for newly-constructed stations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.281-282
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• 2021

본 논문에서는 항공 수요예측을 위한 빅데이터 기반 플랫폼의 설계 및 실증 결과를 제시한다. 항공 수요예측 통합 플랫폼은 항공산업 관련 데이터를 Open API, RSS Feed, 웹크롤러(Web Crawler) 등을 이용하여 수집 및 분석하여 자체 개발한 항공 수요예측 알고리즘을 기반으로 결과를 시각화하여 보여주도록 구현되어 있다. 또한, 제안하는 플랫폼의 사용자 인터페이스를 통해 변수 설정을 하여 단위별(Global, National 등), 기간별(단기, 중장기 등), 유형별(여객, 화물 등) 예측 통계 자료를 도출할 수 있다. 플랫폼의 성능 검증을 위해 정형화된 데이터를 비롯하여 소셜네트워크서비스(SNS), 검색엔진 등에서 수집한 비정형 데이터까지 활용하여 특정 키워드의 빈도와 특정 노선에 대한 항공 수요간 상관관계를 분석하였다. 개발한 통합 플랫폼의 지능형 항공 수요예측 알고리즘을 통해 전반적인 공항 운영 및 공항 운영 정책 수립에 기여할 것으로 예상한다.

• Journal of Korea Port Economic Association
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• v.22 no.4
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• pp.81-98
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• 2006

Many studies on port tariff have been done over twenty years using publicly assessed data on tariff. Public data for tariff rates do not reflect, however, the port tariff in a real market, since the cargo handling charge, which is the important fraction of port tariff, is confidentially decided by the negotiations between a shipping company and a container terminal operator. In this paper, we collected the real price data of the port tariff on the world major sixteen container ports from a global shipping company and transformed it into the tariff per TEU(US$/TEU). The comparative analysis of port tariff was performed using the port tariff per TEU, and a panel regression analysis was done to identify the relations between the port tariff and demand variables: throughput, GDP and trade amount.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.229-240
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• 2012

This study is to analyze effect of differential education for individual end-user group with regard to the computerized tax accounting on its efficient education strategy. In order to achieve the purpose of this study, independent variables and dependent variables on education of computerized tax accounting are measured. Also, discrepancies among individual end-user group on operational satisfaction of computerized system, educational satisfaction and practical utilization which are measured by Regression Analysis are confirmed. This could verify that each end-user group has different understanding for the computerized tax accounting education. The result of this study could be an important guidelines to find a solution for the efficient operational strategy for computerized tax accounting education in future also expected its wide application on other similar studies.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.341-356
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• 2007

We study factors that affect consumers' switching behaviors among service providers in Korean mobile telecommunications service market. For empirical analysis, quarterly time series data from the first quarter of 2004 through the second quarter of 2007 were used. We chose the number of switchers to each mobile service provider in each quarter as dependent variables. Independent variables include acquisition costs per subscriber, which play the role of subsidy to mobile handset, switching costs, time trend, structural change effect, and standby demand effects. Through the empirical analysis, we found that different providers' churn-in customers are affected by different factors. Specifically, the number of chum-in customers into SK Telecom is explained mainly by SK Telecom's customer acquisition costs and standby demand from KTF, while the number of customers switching into KTF is better explained by switching costs from the previous service provider and standby demand from SK Telecom. Those who chose LG Telecom as their new provider, on the other hand, were mainly attracted by LG Telecom's high subscriber acquisition cost.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.63-70
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• 2008

This paper resents the spatial electric load forecasting algorithm using the multiple regression analysis method which is enhanced from the algorithm of the DISPLAN(Distribution Information System PLAN). In order to improve the accuracy of the spatial electrical load forecasting, input variables are selected for GRDP(Gross Regional Domestic Product), the local population and the electrical load sales of the past year. Tests are performed to analyze the accuracy of the proposed method for Gyeong-San City, Gu-Mi City, Gim-Cheon City and Yeong-Ju City of North Gyeongsang Province. Test results show that the overall accuracy of the proposed method is improved the percentage error 11.2[%] over 12[%] of the DISPLAN. Specially, the accuracy is enhanced a lot in the case of high variability of input variables. The proposed method will be used to forecast local electric loads for the optimal investment of distribution systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.57-69
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• 2022

Even though demand to charge EV(electric vehicles) is increasing, there are some problems to construct EV charging stations and problems from deficient them. Typical problem of EV charging stations is discordance for EV charging station location with its demand. This study investigates methods to determine the optimized location for fast EV charging stations considering charging demand in Seoul. Firstly, variables influencing on determination of determine the optimized location for fast EV charging stations were decided, and then evaluation of weights of the variables and data collection were conducted. Using the weights, location potential scores for each area-cell were calculated and optimized locations for fast EV charging stations were resulted.