Earticle

The purpose of this study is to estimate and forecast the tourism demand function,using panel data analysis method. In this study, both inbound and outbound demandare estimated. To be more specific number of tourists from four major countries,USA, Japan, China, and UK to Korea(inbound demand) or from Korea to fourcountries(outbound demand) is estimated using panel data analysis. The empiricalresults show that foreign exchange rate(FX), income level(GDP: gross domesticproduct), and price level(CPI: consumer price index) are important factors having astrong influence on tourism demand. In result, the time-serial and cross-sectionalrelationship between four big countries and Korea is well explained byinbound/outbound tourism demand model estimated by panel data analysis method.

Many countries has been deploying the various policy for international business herb. Korea government has been pursued the free economic zone policy for fostering international business competency in the Northeast Asian. The Yellow Sea Free Economic Zone has been designated in 2007, vision is the high-tech hub of international cooperation, the main purpose are contained creating high-tech industry cluster development, fostering cutting-edge industry, export to China, and advanced value-added logistics. National Industrial Complex 3 and 4 Chungnam province's industrial parks is located in the Yellow Sea Free Economic Zone within 15 ㎞ Radius, and industrial integration of the northwest region is increasing. However, the key infrastructure to support the logistics facility in the Yellow Sea Free Economic Zone is very insufficient and the logistics hub development in the Chungnam western region is very weak. The purpose of this study, first, we will review needs about the west coast logistics hub. Second, we will suggest the size and nature granted yards of logistic park. Third, we will set up the introduction feature of logistics park. To achieve the purpose, we will analyze the transportation demand forecasting, environment of the surrounding area, and linkages the Yellow Sea Free Economic Zone and other area. We will propose the necessity of logistic park composition through this analysing.

본 논문에서는 단기 수요예측방법으로 ARIMA모형을 소개하며, 실전에서 이 모형을 설정하기 위해서 필요한 경험적인 전략에 초점을 두었다. 이 실전전략을 통해 프로세서 내에 복잡하게 얽혀있는 메커니즘을 단순용이하게 설명 및 규명할 수 있으며, 더 나아가 기업의 판매계획, 생산계획, 인사계획, 재무계획 등에 필요한 의사결정을 하는데 도움이 될 수 있다.

In this study, ARIMA model is introduced, which is one of the widely used short-term demand forecasting methods and empirical strategies which can be applied to the real fields are focused. We can easily and simply explain and/or diagnose the mechanism of process and furthermore obtain the important information to make decisions by applying these 31 strategies.

본 연구는 2003년 1사분기부터 2016년 2사분기 까지 인천국제공항에서 미주노선을 통하여 미주 내 공항에 도착하는 항공화물의 시계열 자료를 통하여 SARIMA 모형을 활용하여 항공화물 수요예측을 시행하였다. 또한 SARIMA 모형을 활용하여 만들어진 수요예측 모형과 기존 연구에 주로 활용되어졌던 ARIMA 모형을 활용하여 만들어진 수요예측 모형과 비교분석함으로써, 주기적인 특성 및 계절성을 가진 시계열 자료에 대한 SARIMA 모형의 상대적으로 우수한 예측 정확성을 입증하였다. 기존의 항공 관련 연구는 주로 여객에 관한 연구가 상대적으로 많았다. 또한 화물과 관련된 연구에서도 특정노선이 아닌 공항이나 전체에 대한 연구가 대부분이었다. 이러한 상황에서, SARIMA 모형을 활용하여 미주지역이라는 특정 노선에 대한 항공화물의 수요를 예측한 본 연구는 큰 의의가 있다고 생각된다.

For forecasting air cargo demand from Incheon National Airport to all of airports in the United States (US), this study employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) method and the time-series data collected from the first quarter of 2003 to the second quarter of 2016. By comparing the SARIMA method against the ARIMA method, it was found that the SARIMA method performs well, relatively with time series data highlighting seasonal periodic characteristics. While existing previous research was generally focused on the air passenger and the air cargo as a whole rather than specific air routes, this study emphasized on a specific air cargo demand to the US route. The meaningful findings would support the future research.

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS : This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City's O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.

The current representative temperature selection method with five cities cannot reflect the sufficient regional climate characteristics. In this paper, the new representative temperature selection method is proposed with the consideration of eight representative cities. The proposed method considered the recent trend of power sales, the climate characteristics and population distribution to improve the accuracy of short-term load forecasting. Case study results for the accuracy of short-term load forecasting are compared for the traditional temperature weights of five cities and the proposed temperature weights of eight cities. The simulation results show that the proposed method provides more accurate results than the traditional method.

Since the 90's, Korean Air transport market has been more expanded because of economic growth, the construction of airport infrastructure, and the advent of low cost carrier. Especially, the air traffic demand in Busan metropolitan area has been increasing steadily. Therefore, in this paper, we developed a new forecasting model which could expect the future air traffic demand in Busan area. This model is developed by regression analysis using social-economic variables such as GRDP, income, and the number of people, and dummy variables, for instance, KTX opening, Japan economic depression, SARS and so on. Result from demand forecasting by this new model suggests that the new airport system is needed in order to sustain the increasing air traffic demand in Busan area.

This study aims to build a model dealing with the location decision of new manufacturing firms and their land demand. The model is composed with 1) the binary logit model structure identifying a future probability of manufacturing firms to locate in a city and their land demand; and 2) the land use suitability of the land demand. The model was empirically tested in the case of Anseong City. We used establishment-level data for the manufacturing industry from the Report on Mining and Manufacturing Survey. 48 industry groups were scrutinized to find the location probability in the city and their land demand via logit model with the dependent variables: number of employment, land capital, building capital, total products, and value-added for a new industry since 2001. It is forecasted that the future land areas (to 2025) for the manufacturing industries in the city are $5.94km^2$ and additional land demand for clustering the existing industries scattered over the city is $2.lkm^2$. Five industrial complex locations were identified through the land use suitability analysis.

This paper presents multiple regression analysis and data update to improve present spatial electric load forecasting algorithm of the DISPLAN. Spatial electric load forecasting considers a local economy, the number of local population and load characteristics. A Case study is performed for Jeon-Ju and analyzes a trend of the spatial load for the future 20 years. The forecasted information can contribute to an asset management of distribution systems.

본 연에서는 현재 전력거래소에서 사용하고 있는 단기수요예측 산법을 전력시장 운영환경에 대응 하도록 보다 정확하면서도 공정성이 보장되는 산법으로 개선하였다. 접근방법은 기존의 산법들을 면밀히 분석한 다음 산법의 개선과 매개변수의 튜닝을 통하여 예측정확도를 개선하였으며, 예측과정의 투명성을 확보하기 위하여 예측절차를 출력하는 기능을 포함하였다 예측정확도를 개선하기 위한 주요 방안으로 종합분석모형의 경우는 실적자료가 생길 때 마다 즉시 민감도가 갱신되도록 하였으며, 회귀분석모형은 분석과정에서 의미가 있는 자료만을 선택하도록 하였다. 또한 신경망 모형의 경우는 모의를 통하여 최적의 입력변수를 찾아 설정하였으며, 지식기반모형에서는 최근의 수요특성을 분석하여 새로운 규칙들로 구축하였다. 제안한 산법의 효용성을 평가하기 위하여 2004년도 실계통 자료를 대상으로 모의를 해 본 결과, 모든 산법에서 개선된 예측정확도를 나타내었다.

The aged population in Korea, especially in rural areas, has been growing rapidly. The welfare for the rural elderly has become a major concern, however we don't have enough information about that population; we don't even have detailed demographics of it. The present research is aimed at; 1) investigating the tendency and changing geographical distributions of the rural elderly, and 2) introducing GIS(Geographic Information Systems) as a useful tool in analyzing geographical distributions of the aged. The General Census Data from 1960 to 2000 was used to carry out this study. The major findings are; 1) There has been a gradual decrease in the rural population over the past 40 years, but this tendency has slowed down more recently; 2) It was observed that the 'oldest-old' group aged 85 years old and over has actuality increased in rural areas faster than any other age group; 3) The changing patterns of the elderly population were different in metropolitan areas and rural areas. In brief, there are far more aged people, especially of the 'oldest-old' group and females in rural areas than those in urban areas. These population, the 'oldest-old' or females, are the most vulnerable and have the greatest need for social welfare and social services of different kinds.

Railway passenger demand forecasts may be used directly, or as inputs to other optimization models use them to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

전력계통의 수요예측은 수십 년간의 일별, 주별, 월별, 년도별 자료와 기타 수많은 계수들을 요구하므로 처리해야할 자료의 양이 방대하여, 수요예측에는 데이터베이스의 사용이 필수이다. 본 연구에서는 수요예측 및 이와 유사한 대규모 자료의 전산화에 적합한 데이터베이스 설계기법을 소개하고, 계산 수행 시 속도 및 운용의 효율성을 기하기 위한 방안을 소개한다. 또한 데이터베이스의 유지보수를 위한 기법과 각종 접근 방법의 예를 들었다.

Heuksando is an island which belongs to Shinangun, Jeonllanamdo and is located on the southwest sea of the Korean peninsula. Around this island, there are many beautiful islands which embroider the archipelago such as Hongdo, Soheuksando, Haeuido, Gageodo. However in the transportation mode we could not offer convenience to all the visitors coming to this area because access to this place can be made only by ship from Mokpo harbor. So new airport is desirable to solve this problem in this area. Therefore, this study is forecasting air transportation demand between Heuksando and several domestic places in order to give the fundamental materials not only to address the appropriateness to construct a new airport but also to determine it's size and necessary facilities.

과거 오랫동안 기업가나 팝슨(1979) 등의 학자들에 의해서 21세기의 가장 큰 산업이 될 것이라고 예측된 관광산업은 오늘날 단일산업으로는 세계 최대 규모이다. 관광분야에 대한 연구 중, 관광현상을 과학적이고 객관적으로 분석할 수 있는 방법론을 연구하고 제시하며 응용하는 분야로 관광조사분석론을 들 수 있다. 그런데 여기서 다루어지는 내용 중 관광수요와 마케팅분야가 핵심분야로 여겨진다. 오늘날 경제성장에 기인하여 해외관광은 급격한 성장을 보이고 있다. 이러한 상황에서, 관광마케팅을 위한 올바른 관광정책 수립에 필요한 해외관광 수요예측이 요구되는 바, 본 논문에서는 이러한 해외관광 수요예측을 위 한 중력모형과 회귀모형 에 대해서 비교해 보았다. 또한 중력모형을 이용한 수요 예측 시 우리실정에 맞는 여행유입요소를 제안하고, 이를 이용한 모형 비교분석을 실시해 보았다.

Now a day, overseas tour which is due to economic development grows very much. In this situation, a forecast of overseas tour is required to establish tourism policy for tourism marketing. In this paper, we compare regression model and gravity model for a forecast of overseas tour. Using gravity model, this study also suggests an attraction which is suitable to our situation, and suggested attraction is compared and analyzed with another.

Railway passenger demand forecasts may be used directly, or as inputs to other optimization model which is use the demand forecasts to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

본 논문은 수요 예측함에 있어서 여러 가지 수요 예측 방법을 통해 매 시기 마다 적절한 수요예측 기법을 사용하여 좀더 정확한 수요예측 결과를 추정하기 위한 방법을 연구하며 특히, 수요 예측하기 어려운 제품에 대해 여러 인자를 고려하여 좀더 나은 예측치를 구하기 위한 방법을 연구하고 있다. 마지막으로 각 ERP나 SCM, MRP application에 연계하여 필요한 자료를 되게 얻고 이를 다시 보내 줄 수 있는 일반적인 연계 방법을 연구하고 있다. 본 논문에서는 데이터 베이스 연계부분에서는 ODBC 를 사용하였으며, 예측 기법은 Moving Average 기법과 Exponential Smoothing 기법, 그리고 Neural Networks 중 BP 를 이용하여 구현하였다. 앞으로 좀 더 많은 예측 기법을 적용하여 향상된 수요 예측을 위한 모듈을 연구 및 구현하려 한다.

This paper proposes a new short-term load forecasting method for special day, such as Public holidays, consecutive holidays, and days before and after holidays. when the load curves are quite different from those of normal weekdays. In this paper, two Artificial Neural Network(ANN) systems are applied to short-term load forecasting for spacial days in anomalous load conditions.