• 도시과학
• /
• 제7권1호
• /
• pp.45-54
• /
• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• ETRI Journal
• /
• 제44권4호
• /
• pp.624-640
• /
• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

• 스마트미디어저널
• /
• 제12권10호
• /
• pp.9-18
• /
• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• 한국도로학회논문집
• /
• 제18권3호
• /
• pp.47-57
• /
• 2016

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.

• Asia Pacific Journal of Business Review
• /
• 제5권1호
• /
• pp.1-9
• /
• 2020

Tourism development stimulates job creation and the development of other sectors of the economy. More than 30 sectors of the economy are connected to tourism. It distributes resources between sectors and stimulates of development of such sectors like transport, communications, services, trade, construction, and the production of consumer goods. All these increase the importance of tourism as well as forecasting it by analyzing the demand. This study is a review on inbound tourism of Uzbekistan. The study will examine regression analysis as an effective tool that plays an important role as well as in the field of tourism demand analysis. In this study, firstly the estimating tourism demand is explained, secondly, the regression analysis is examined as an estimating tool of tourism demand. The paper includes a country study dedicated to the Tourism market of Uzbekistan. Nevertheless, the forecast on the inbound tourism of Uzbekistan was developed by using some statistical data.

• 융복합기술연구소 논문집
• /
• 제9권1호
• /
• pp.31-36
• /
• 2019

After 30 years of KKP model analysis and extended 30 years of accuracy analysis, the unique correlation and various problems between measured horizontal surface insolation and measured weather data are found in this paper. The KKP model's 10yrs daily total horizontal surface insolation forecasting was averaged about 97.7% on average, and the forecasting accuracy at peak times per day was about 92.1%, which is highly applicable regardless of location and weather conditions nationwide. The daily total solar radiation forecasting accuracy of the modified KKP cloud model was 98.9%, similar to the KKP model, and 93.0% of the forecasting accuracy at the peak time per day. And the results of evaluating the accuracy of calculation for 30 years of KKP model were cloud model 107.6% and cloud model 95.1%. During the accuracy analysis evaluation, this study found that inaccuracies in measurement data of cloud cover should be clearly assessed by the Meteorological Administration.

• Nuclear Engineering and Technology
• /
• 제49권5호
• /
• pp.1063-1070
• /
• 2017

This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.

• Journal of Nutrition and Health
• /
• 제36권9호
• /
• pp.966-973
• /
• 2003

The purpose of this study was to investigate the utilization level of forecasting methods in contract foodservice management companies. Questionnaires were distributed and collected from 30 foodservice management companies contracted with universities and 49 university foodservices in Seoul and Kyungki area. Statistical data analysis was performed using SPSS/WIN 10.0 based on the production records of Yonsei University foodservices and the weather reports from a meteorological observatory. The results of this study were as follows: 1) The objectives of the fore-casting systems were identified as saving costs through eliminating the leftover, meeting the customer demands, and improving efficiency in food preparation.2) All of the university foodservices were already performing the forecasting methods but in foodservice management companies as a whole,89.7 percents were applying the method and only 55.2 percents had the separate forecasting department. 3) A large number of foodservice staffs in the head office (65.5％) answered that they often utilized intuitive estimates based on the past experiences and records for forecasting while 65.3% managing staffs in the university foodservices answered the same.4) Both in the head office and university foodservices, actual number of meals served were recorded. In the head office, mostly estimated numbers and actual numbers of meals were recorded while estimated, prepared, and actual numbers of meals served were recorded for most of the cases in university foodservices. 5) The primary factors considered for forecasting were the actual production records for the last month, the customer preference for the selected menu items, and the specific day of the week.

• 전기학회논문지
• /
• 제62권2호
• /
• pp.149-154
• /
• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• 한국환경과학회지
• /
• 제22권8호
• /
• pp.965-977
• /
• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.