• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.117-134
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• 2004

Truck traffic accounts for a substantial fraction of the traffic stream in many regions and is often the source of localized traffic congestion, potential parking and safety problems. Truck trips tend to be ignored or treated superficially in travel demand models. It reduces the effectiveness and accuracy of travel demand forecasting and may result in misguided transportation policy and project decisions. This paper presents the development of speed-flow relationships with truck impacts based on CORSIM simulation results in order to enhance travel demand model by incorporating truck trips. The traditional BPR(Bureau of Public Road) function representing the speed-flow relationships for roadway facilities is modified to specifically include the impacts of truck traffics. A number of new speed-flow functions have been developed based on CORSIM simulation results for freeways and urban arterials.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.465-475
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• 2011

In this paper, we have considered the problems of the estimation of the gross areas of a factory site relating to the areas of industrial complex lands based on a statistical forecasting model and the results of a sampling survey. In respect to the data of a gross areas of a factory site, we have only the sizes from 1981-2003. In 2009, the Korea Industrial Complex Corp. conducted a sampling survey to estimate its bulk size, and investigate the demands of its sizes for the next five years. In this study, we have adopted the sampling survey results, and have created a statistical growth model for the gross areas of a factory site to improve the prediction for the areas of a factory site. The three-different parts of data: the results of areas of a factory site by Korea National Statistical Office, imputation results by the statistical forecasting model, and sampling survey results have used as the basis for analysis. The combination of the three-different parts of data has created a new forecasting value of the areas of a factory site through the spline smoothing method.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.37-51
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• 2009

One of the critical issues in the management of manufacturing companies is the efficient process of planning and operating service resources such as human, parts, and facilities, and it begins with the accurate service demand forecasting. In this research, service and sales data from the LCD monitor manufacturer is considered for an empirical study on Product Life Cycle (PLC) based service demand forecasting. The proposed PLC forecasting approach consists of four steps : understanding the basic statistics of data, clustering models using a self-organizing map, developing respective forecasting models for each segment, comparing the accuracy performance. Empirical experiments show that the PLC approach outperformed the traditional approaches in terms of root mean square error and mean absolute percentage error.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.360-365
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• 2003

In this paper, GMDH(Croup Method of Data Handling) algorithm which is proved to be more excellent in efficiency and accuracy of practical use of data is applied to electric power demand forecasting. As a result, it became much easier to make a choice of input data and make an exact prediction based on a lot of data. Also, we considered both economy factors(GDP, export, import, number of employee, number of economically active population and consumption of oil) and climate factors(average temperature) when forecasting. We assumed target forecast period from first quarter 1999 to first quarter 2001, and suggested more accurate forecasting method of electric power demand by using 3-step computer simulation processes(first process for selecting optimum input period, second for analyzing time relation of input data and forecast value, and third for optimizing input data) for improvement of forecast precision. The proposed method can get 0.96 percent of mean error rate at target forecast period.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.953-955
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• 2020

공컨테이너(Empty Container)는 적컨테이너(Full Container)와 달리, 화물이 적재되지 않은 비어있는 컨테이너로 공컨테이너 재고는 수출에 비해 수입이 많은 항만에서, 수요는 수입에 비해 수출이 많은 항만에서 발생한다. 그러나 수입과 수출은 기간, 지역에 따라 유동적이기 때문에 수요와 재고량 예측에 어려움이 있는데, 본 연구에서는 자기회귀누적이동평균(ARIMA)과 머신러닝 기법을 활용하여 이를 예측하는 방법을 제시한다. 본 연구에 활용된 데이터와 프로그램 소스코드는 Kaggle 에 공개되어 있다.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.521-528
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• 2016

The tollbooths of a main motorway toll plaza are usually operated proactively responding to the variations of traffic demands of two-type vehicles, i.e. cars and the other (heavy) vehicles, respectively. In this vein, it is one of key elements to forecast accurate traffic volumes for the two vehicle types in advanced tollgate operation. Unfortunately, it is not easy for existing univariate short-term prediction techniques to simultaneously generate the two-vehicle-type traffic demands in literature. These practical and academic backgrounds make it one of attractive research topics in Intelligent Transportation System (ITS) forecasting area to forecast the future traffic volumes of the two-type vehicles at an acceptable level of accuracy. In order to address the shortcomings of univariate short-term prediction techniques, a Multiple In-and-Out (MIO) forecasting model to simultaneously generate the two-type traffic volumes is introduced in this article. The MIO model based on a non-parametric approach is devised under the on-line access conditions of large-scale historical data. In a feasible test with actual data, the proposed model outperformed Kalman filtering, one of a widely-used univariate models, in terms of prediction accuracy in spite of multivariate prediction scheme.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.06a
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• pp.128-140
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• 2001

현재 국내의 이동통신 가입자수와 무선인터넷 가입자수가 급증하면서 인터넷과 이동 통신을 연계한 광고서비스나 직접 휴대폰에 광고를 제공하는 서비스가 등장하고 있다. 그러나 현재 이동통신에 제공되고 있는 광고서비스는 문자메세지를 이용하는 단문광고이고 제공되는 광고도 휴대폰 이용자가 직접 듣고 확인해야 하는 불편함을 가지고 있다. 따라서 본 연구에서는 이동통신을 활용하여 고객의 니즈(Needs)에 부합되는 광고를 제공할 수 있는 새로운 광고기법을 제안하고 이에 대한 수요예측과 데이터 마이닝 기법을 적용하여 이 광고에 대한 타겟마케팅 전략을 제시하고자 한다.