본 연구는 국내 도로사업의 교통수요 예측오차를 종합적으로 평가하고, 보다 효율적인 기대교통량 추정모형의 개발을 목적으로 수 행되었다. 이를 위해 본 연구에서는 1999년부터 2010년까지 수행된 예비타당성조사 및 타당성재조사 사업들 가운데 62건의 도로사업 (690개 구간)의 자료를 활용하였다. 본 연구의 주요 특징은 다음과 같다. 첫째, 기존 연구들과 달리 사업구간 뿐만 아니라 주변구간을 포함하여 교통수요 예측 오차를 평가했다는 점이다. 둘째, 본 연구는 교통수요 예측의 오차를 정확성, 추정편의, 추정연계성 등 다양한 평가지표를 활용하여 분석했다는 점이다. 실측자료를 통한 분석결과, 전체구간의 평균 백분율 오차(MPE)는 11.6%(과소추정)로 파악되 었지만, 이를 사업구간과 주변구간으로 나누어 살펴보면, 사업구간의 경우 -13.5%(과다추정), 주변구간은 16.5%(과소추정)로 상반된 결 과를 나타내었다. 추정편의 분석결과, 전체구간에서는 통계적으로 유의미한 편의가 발견되지 않았으나, 사업구간과 주변구간 각각에서 는 편의가 존재하는 것으로 나타났다. 추정연계성 분석에서는 주변구간의 경우 기준연도 정산 결과와 개통연도 오차 간 유의미한 관 계가 확인되었다. 이러한 분석결과를 바탕으로, 본 연구는 분위회귀모형을 활용한 기대교통량 추정모형을 제안하였는데, 이는 기존의 점 추정 방식의 한계를 보완하는 방안이다. 이 모형은 사업구간과 주변구간을 구분하여 개발되었으며, 실측교통량의 50% 분위를 중심 으로 95% 신뢰구간을 제시하였다. 또한, 동 모형에서는 고속도로 여부, 준공 지연 기간 등 주요 변수들의 영향을 고려하여 모형의 설 명력을 높였다는 특징을 갖는다. 본 연구의 결과는 도로사업의 교통수요 예측 정확성 향상과 투자 의사결정의 합리성 제고에 기여 할 수 있을 것으로 기대된다. 특히, 제안된 기대교통량 추정 모형은 예비타당성조사 등에서 보다 현실적인 교통수요 예측치를 제공하고, 이를 통해 경제성 분석의 신뢰도를 높이는 데 활용될 수 있을 것이다. 또한, 사업구간과 주변구간의 교통량 변화 특성이 다르다는 점 을 고려하여, 향후 도로 사업의 영향 평가 시 보다 세밀한 접근이 필요함을 시사한다.

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor’s item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

최근 국민 삶의질 향상, 여가 활동 다변화, 인구구조의 변화 등으로 관광수요 증가 및 관광활동이 다양화되고 있다. 특히 연안 도시의 경우, 육상 관광 요소와 해양관광 요소가 공존하는 지역으로 다양한 요인이 관광수요에 영향을 미치고 있다. 본 연구 목적은 본 연구는 행위자 기반의 데이터를 활용하여 관광규모의 시계열 분석을 통해 예측 정확도를 향상시키고, 영향요인을 탐색하고자 한다. 연구 대상은 부산 지역 내 기초자치단체이며, 데이터는 월단위의 관광객수와 관광소비금액을 활용하였다. 연구방법으로 확정적(결정적) 모형 인 단변량 시계열 분석과 영향요인을 파악하기 위해 SARIMAX 분석을 수행하였다. 영향요인은 관광소비성향을 설정하였으며, 업종별 소 비금액과 SNS 언급량을 중심으로 설정하였다. 연구결과 COVID-19를 고려하지 않은 시계열 모형과 고려한 모형 간의 정확도(RMSE 기준) 차이가 지역별로 최소 1.8배에서 최대 32.7배 향상되었다. 또한 영향요인을 보면 관광소비업종과 SNS 트렌드가 관광객수와 관광소비금액 에 유의한 영향을 미치고 있다. 따라서 미래 수요예측을 위해서는 외적 영향을 고려하고, 관광객의 소비성향과 관심도가 지역관광 측면에 서 고려 대상이 된다. 본 연구는 연안도시인 부산 지역의 미래 관광수요 예측과 관광규모에 미치고 있는 영향요인을 파악하여 정부 관광 정책 및 관광추세를 고려한 관광수요태세 마련을 위한 정책 의사결정에 기여하고자 한다.

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea’s SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

PURPOSES : The primary purpose of this study is to develop a framework for predicting the demand and distribution of pedestrians when an open space zone is built at the top through the undergroundization of the Gyeongin Expressway. METHODS : After analyzing the current status through a survey on the number of people, students, surrounding traffic volume, and future socioeconomic indicators, the rate of change in the floating population and the rate of increase and decrease in the traffic volume of pedestrians were calculated to evaluate the effect. In addition, microscopic analysis results were derived by setting a pedestrian analysis zone (PAZ). A walking environment index (WEI) was developed that can quantitatively evaluate the degree of walking activation by indicating walking-related surrounding environmental factors. Based on this, a walking demand prediction model was developed. In addition, the results were validated by calculating the walking volume through a micro-simulation in/around the open space zone. RESULTS : The number of crosswalks and schools, transit development indicators, and pedestrian volume increased as the WEI value increased. However, the log form of the distance was observed to be a factor that reduced walking. CONCLUSIONS : This study attempted to reliably predict the demand for walking on the Gyeongin Expressway by calculating the amount of induced walking and the amount of passing walking. The pedestrian demand can be boosted by improving walking environments.

본 연구는 전주시에 위치한 기린봉, 완산칠봉, 황방산 등 3개의 산지형 근린공원을 대상으로 이용행태, 이용권의 인구 통계학적 특성, 근린공원까지의 도달거리를 사용하여 유효이용수요를 예측한 것이다. 조사대상 3개 근린공원의 이용자는 주로 40대~60대 이상이었으며, 이용방법은 주로 도보로 근린공원에 도달하였다. 방문횟수는 주간 1~2회 정도로 나타났다. 조사대상 근린공원의 유효 이용권을 1,000m로 설정하여 변형 중력모델에 의해 이용수요를 예측한 결과 기린봉 근린공원은 4,500명/일, 완산칠봉 근린공원은 3,159명/일, 황방산 근린공원은 2,961명/일이었다. 전체 유효 이용수요는 기린봉 근린공원이 가장 많이 나타났으나, 면적대비 유효이용수요는 완산칠봉 근린공원이 가장 높게 나타났다.

정부는 공유수면 매립사업의 계획적인 관리를 위해, 10년 주기의 공유수면 매립기본계획을 수립하고 있다. 그러나 수시변경을 통한 매립사업을 추진하는 경우가 상당한 비중을 차지하고 있는 것으로 나타났다. 이에 기본계획의 실효성에 대한 의문이 제기되고 있으 며, 이를 보완하기 위한 장기 매립 수요 추세 분석에 대한 필요성이 증가하고 있다. 이에 본 연구에서는 그간의 연간 매립 실적 자료를 활용하여 매립 수요 추세 분석을 수행하였다. 분석 결과, 국내 공유수면 매립 수요는 지속적으로 하락하는 추세인 것으로 나타났으며, 특 히 매립기본계획 체제로 전환된 1990년대 이후에는 그 추세가 뚜렷하게 나타나고 있는 것으로 나타났다. 또한 2021-2030년까지 총 매립 수요는 최대 13.8 km2에서 최소 1.7 km2 수준으로 산정되었다.

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

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).

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.