Relevant bibliographies by topics / Demand prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Demand prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Demand prediction"

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Thiagarajan, Rajesh, Mustafizur Rahman, Don Gossink, and Greg Calbert. "A Data Mining Approach To Improve Military Demand Forecasting." Journal of Artificial Intelligence and Soft Computing Research 4, no. 3 (July 1, 2014): 205–14. http://dx.doi.org/10.1515/jaiscr-2015-0009.

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Abstract Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.
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Tian, Wen, Ying Zhang, Yinfeng Li, and Huili Zhang. "Probabilistic Demand Prediction Model for En-Route Sector." International Journal of Computer Theory and Engineering 8, no. 6 (December 2016): 495–99. http://dx.doi.org/10.7763/ijcte.2016.v8.1095.

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Chen, Zhiju, Kai Liu, and Tao Feng. "Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects." Journal of Advanced Transportation 2022 (April 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/7690309.

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The accurate short-term travel demand predictions of ride-hailing orders can promote the optimal dispatching of vehicles in space and time, which is the crucial issue to achieve sustainable development of such dynamic demand-responsive service. The sparse demands are always ignored in the previous models, and the uncertainties in the spatiotemporal distribution of the predictions induced by setting subjective thresholds are rarely explored. This paper attempts to fill this gap and examine the spatiotemporal sparsity effect on ride-hailing travel demand prediction by using Didi Chuxing order data recorded in Chengdu, China. To obtain the spatiotemporal characteristics of the travel demand, three hexagon-based deep learning models (H-CNN-LSTM, H-CNN-GRU, and H-ConvLSTM) are compared by setting various threshold values. The results show that the H-ConvLSTM model has better prediction performance than the others due to its ability to simultaneously capture spatiotemporal features, especially in areas with a high proportion of sparse demands. We found that increasing the minimum demand threshold to delete more sparse data improves the overall prediction accuracy to a certain extent, but the spatiotemporal coverage of the data is also significantly reduced. Results of this study could guide traffic operations in providing better travel services for different regions.
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Lee, Eunkyeong, Hosik Choi, and Do-Gyeong Kim. "PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport." Journal of Advanced Transportation 2023 (January 5, 2023): 1–13. http://dx.doi.org/10.1155/2023/7152010.

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To provide an efficient demand-responsive transport (DRT) service, we established a model for predicting regional movement demand that reflects spatiotemporal characteristics. DRT facilitates the movement of restricted passengers. However, passengers with restrictions are highly dependent on transportation services, and there are large fluctuations in travel demand based on the region, time, and intermittent demand constraints. Without regional demand predictions, the gaps between the desired boarding times of passengers and the actual boarding times are significantly increased, resulting in inefficient transportation services with minimal movement and maximum costs. Therefore, it is necessary to establish a regional demand generation prediction model that reflects temporal features for efficient demand response service operations. In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. The proposed model considers a region’s unique characteristics and the influence between regions through spatial information, such as the proximity between regions, convenience of transportation, and functional similarity. In addition, three types of temporal characteristics—adjacent visual characteristics, periodic characteristics, and representative characteristics—were defined to reflect past demand patterns. With the proposed demand forecasting model, measures can be taken, such as having empty vehicles move to areas where demand is expected or encouraging adjustment of the vehicle’s rest time to avoid congestion. Thus, fast and efficient transportation satisfying the movement demand of passengers with restrictions can be achieved, resulting in sustainable transportation.
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Kim, Sujae, Sangho Choo, Gyeongjae Lee, and Sanghun Kim. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method." Sustainability 14, no. 5 (February 23, 2022): 2564. http://dx.doi.org/10.3390/su14052564.

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The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.
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Acakpovi, Amevi, Alfred Tettey Ternor, Nana Yaw Asabere, Patrick Adjei, and Abdul-Shakud Iddrisu. "Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems." Mathematical Problems in Engineering 2020 (August 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/4181045.

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This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.
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Mi, Chunlei, Shifen Cheng, and Feng Lu. "Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks." ISPRS International Journal of Geo-Information 11, no. 3 (March 9, 2022): 185. http://dx.doi.org/10.3390/ijgi11030185.

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Predicting taxi-calling demands at the urban area level is vital to coordinate the supply–demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand dependence pose challenges to predicting demand. Here, a framework using residual attention graph convolutional long short-term memory networks (RAGCN-LSTMs) is proposed to predict taxi-calling demands. It consists of a spatial dependence (SD) extractor, which extracts SD features; an external dependence extractor, which extracts traffic environment-related features; a pattern dependence (PD) extractor, which extracts the PD of demands for different zones; and a temporal dependence extractor and predictor, which leverages the abovementioned features into an LSTM model to extract temporal dependence and predict demands. Experiments were conducted on taxi-calling records of Shanghai City. The results showed that the prediction accuracies of the RAGCN-LSTMs model were a mean absolute error of 0.8664, a root mean square error of 1.4965, and a symmetric mean absolute percentage error of 43.11%. It outperformed both classical time-series prediction methods and other deep learning models. Further, to illustrate the advantages of the proposed model, we investigated its predicting performance in various demand densities in multiple urban areas and proved its robustness and superiority.
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Xu, Long Jun, Dong Mei Chen, Li Li, and Yi Ming Feng. "Trends Analysis on Manganese Demand by GM(1,1)." Advanced Materials Research 347-353 (October 2011): 2815–18. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2815.

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Based on the actual data of Xiushan County and Chinese manganese demand, GM (1, 1) model was established to predict the manganese demand. The prediction accuracies of Xiushan’s manganese demand, Chinese manganese production and exports were respectively 95.5%, 94.5% and 96.3%. Residual error test showed that the model was reliable and could be used to predict the manganese demands in the future five years. And the demands would be grown exponentially with time. The predictive results indicated that the supply safeguard was worrying.
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Maltais, Louis-Gabriel, and Louis Gosselin. "Predicting Domestic Hot Water Demand Using Machine Learning for Predictive Control Purposes." Proceedings 23, no. 1 (August 26, 2019): 6. http://dx.doi.org/10.3390/proceedings2019023006.

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An important part of a building energy consumption is related to the domestic hot water consumption of its occupants. Predictive controllers are often considered as having the potential to reduce the energy consumption of hot water systems. In this work, a recurrent neural network is trained from the measured domestic hot water consumption of a 40 unit residential building in Quebec City, Canada, to predict the future consumption. It is found that the water consumption profile of the building changes from day to day throughout the year and has an important noise component. A predicting model is developed in this work and is obtained by pairing a recurrent neural network to predict the filtered domestic hot water demand with a random forest to predict the noise signal. The evaluated performances indices for the prediction of the next demand are satisfying (i.e., RMSE of 142.02 L/h and R2 of 0.71). In addition, it is found that the predictions made over the following hour using the same predicting model are accurate and could likely be used in a predictive control context.
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Takahashi, K., R. Ooka, and S. Ikeda. "Anomaly detection and missing data imputation in building energy data for automated data pre-processing." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012144. http://dx.doi.org/10.1088/1742-6596/2069/1/012144.

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Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.
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Dissertations / Theses on the topic "Demand prediction"

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McElroy, Wade Allen. "Demand prediction modeling for utility vegetation management." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117973.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 63-64).
This thesis proposes a demand prediction model for utility vegetation management (VM) organizations. The primary uses of the model is to aid in the technology adoption process of Light Detection and Ranging (LiDAR) inspections, and overall system planning efforts. Utility asset management ensures vegetation clearance of electrical overhead powerlines to meet state and federal regulations, all in an effort to create the safest and most reliable electrical system for their customers. To meet compliance, the utility inspects and then prunes and/or removes trees within their entire service area on an annual basis. In recent years LiDAR technology has become more widely implemented in utilities to quickly and accurately inspect their service territory. VM programs encounter the dilemma of wanting to pursue LiDAR as a technology to improve their operations, but find it prudent, especially in the high risk and critical regulatory environment, to test the technology. The biggest problem during, and after, the testing is having a baseline of the expected number of tree units worked each year due to the intrinsic variability of tree growth. As such, double inspection and/or long pilot projects are conducted before there is full adoption of the technology. This thesis will address the prediction of circuit-level tree work forecasting through the development a model using statistical methods. The outcome of this model will be a reduced timeframe for complete adoption of LiDAR technology for utility vegetation programs. Additionally, the modeling effort provides the utility with insight into annual planning improvements. Lastly for later usage, the model will be a baseline for future individual tree growth models that include and leverage LiDAR data to provide a superior level of safety and reliability for utility customers.
by Wade Allen McElroy.
M.B.A.
S.M.
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Zhou, Yang. "Multi-Source Large Scale Bike Demand Prediction." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703413/.

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Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.
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Sun, Rui S. M. Massachusetts Institute of Technology. "Analytics for hotels : demand prediction and decision optimization." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111438.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-71).
The thesis presents the work with a hotel company, as an example of how machine learning techniques can be applied to improve demand predictions and help a hotel property to make better decisions on its pricing and capacity allocation strategies. To solve the decision optimization problem, we first build a random forest model to predict demand under given prices, and then plug the predictions into a mixed integer program to optimize the prices and capacity allocation decisions. We present in the numerical results that our demand forecast model can provide accurate demand predictions, and with optimized decisions, the hotel is able to obtain a significant increase in revenue compared to its historical policies.
by Rui Sun.
S.M. in Transportation
S.M.
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Svensk, Gustav. "TDNet : A Generative Model for Taxi Demand Prediction." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.

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Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and one in South America. In northern europe, an error of one taxi or less per hour per zone was achieved in 64% of the cases, in South America the number was 40%. In both cities, it beat the SARIMA and stacked ensemble benchmarks. This performance has been achieved by tuning the hyperparameters with a Bayesian optimization algorithm. Additionally, weather and holiday features were added as input features in the northern European city and they did not improve the accuracy of TDNet.
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Lu, Hongwei Marketing Australian School of Business UNSW. "Small area market demand prediction in the automobile industry." Publisher:University of New South Wales. Marketing, 2008. http://handle.unsw.edu.au/1959.4/43027.

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The general aim of this research is to investigate approaches to: •improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and •enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: •What are the key challenges in meso-level SAMD prediction? •What variables affect SAMD prediction? •What techniques can be used to improve SAMD prediction? •What is the value of integrating these techniques to improve SAMD prediction? To answer these questions, possible solutions from two broad areas are examined: spatial analysis and data mining. The research is divided into two main studies. In the first study, a seven-step modelling process is developed for SAMD prediction. Several sets of models are analysed to examine the modelling techniques’ effectiveness in improving the accuracy of SAMD prediction. The second study involves two cases to: 1) explore the integration of these techniques and their advantages in SAMD prediction; and 2) gain insights into spatial marketing issues. The case study of Peugeot in the Sydney metropolitan area shows that urbanisation and geo-marketing factors can have a more important role in SAMD prediction than socio-demographic factors. Furthermore, results show that modelling spatial effects is the most important aspect of this prediction exercise. The value of the integration of techniques is in compensating for the weaknesses of conventional techniques, and in providing complementary and supplementary information for meso-level marketing analyses. Substantively, significant spatial variation and continuous patterns are found with the influence of key studied variables. The substantive implications of these findings have a bearing on both academic and managerial understanding. Also, the innovative methods (e.g. the SAMD modelling process and the model cube based technique comparison) developed from this research make significant contributions to marketing research methodology.
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Lönnbark, Carl. "On Risk Prediction." Doctoral thesis, Umeå universitet, Nationalekonomi, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-22200.

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This thesis comprises four papers concerning risk prediction. Paper [I] suggests a nonlinear and multivariate time series model framework that enables the study of simultaneity in returns and in volatilities, as well as asymmetric effects arising from shocks. Using daily data 2000-2006 for the Baltic state stock exchanges and that of Moscow we find recursive structures with Riga directly depending in returns on Tallinn and Vilnius, and Tallinn on Vilnius. For volatilities both Riga and Vilnius depend on Tallinn. In addition, we find evidence of asymmetric effects of shocks arising in Moscow and in the Baltic states on both returns and volatilities. Paper [II] argues that the estimation error in Value at Risk predictors gives rise to underestimation of portfolio risk. A simple correction is proposed and in an empirical illustration it is found to be economically relevant. Paper [III] studies some approximation approaches to computing the Value at Risk and the Expected Shortfall for multiple period asset re- turns. Based on the result of a simulation experiment we conclude that among the approaches studied the one based on assuming a skewed t dis- tribution for the multiple period returns and that based on simulations were the best. We also found that the uncertainty due to the estimation error can be quite accurately estimated employing the delta method. In an empirical illustration we computed five day Value at Risk's for the S&P 500 index. The approaches performed about equally well. Paper [IV] argues that the practise used in the valuation of the port- folio is important for the calculation of the Value at Risk. In particular, when liquidating a large portfolio the seller may not face horizontal de- mandcurves. We propose a partially new approach for incorporating this fact in the Value at Risk and in an empirical illustration we compare it to a competing approach. We find substantial differences.
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Bernhardsson, Viktor, and Rasmus Ringdahl. "Real time highway traffic prediction based on dynamic demand modeling." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112094.

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Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch north of Stockholm. The thesis is focusing on the implementation and evaluation of traffic prediction by running a cell transmission model (CTM) forward in time.This method gives reliable predictions for a prediction horizon of up to 5 minutes. In order to improve the results for traffic predictions, a framework for dynamic inputs of demand and sink capacity has been implemented in the MMS system. The third part of the master thesis presents a model which adjusts the split ratios in a macroscopic traffic model based on driver behavior during congestion.
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Jones, Simon Andrew. "Prediction of demand for emergency care in an acute hospital." Thesis, Kingston University, 2005. http://eprints.kingston.ac.uk/20739/.

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This thesis describes some models that attempt to forecast the number of occupied beds due to emergency admissions each day in an acute general hospital. Hospital bed managers have two conflicting demands: they must not only ensure that at all times they have sufficient empty beds to cope with possible emergency admissions but they must fill as many empty beds as possible with people on the waiting list. This model is important as it could help balance these two conflicting demands. The research is based on data from a district general and a postgraduate teaching hospital in South East London. Several tests indicate that emergency bed occupancy may have a nonlinear underlying data generating process. Therefore, both linear models and nonlinear models have been fitted to the data. At horizons up to 14 days, it was found that there was no statistically significant difference in the errors from the linear and nonlinear models. However at the 35 day forecast horizon the linear model gives the best forecast and tests indicate errors from this model are within 4% of mean occupancy. It is noted that a Markov Switching model gave very good forecasts of up to 4 days into the future. A search of the literature found no previous research that tested emergency bed occupancy for nonlinearities. The thesis ends with a gravity model to predict the change in number of Accident and Emergency (A&E) attendances following the relocation of an A&E Department in South East London.
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Shen, Ni. "Prediction of International Flight Operations at U.S. Airports." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/35687.

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This report presents a top-down methodology to forecast annual international flight operations at sixty-six U.S. airports, whose combined operations accounted for 99.8% of the total international passenger flight operations in National Airspace System (NAS) in 2004. The forecast of international flight operations at each airport is derived from the combination of passenger flight operations at the airport to ten World Regions. The regions include: Europe, Asia, Africa, South America, Mexico, Canada, Caribbean and Central America, Middle East, Oceania and U.S. International.

In the forecast, a "top-down" methodology is applied in three steps. In the fist step, individual linear regression models are developed to forecast the total annual international passenger enplanements from the U.S. to each of nine World Regions. The resulting regression models are statistically valid and have parameters that are credible in terms of signs and magnitude. In the second step, the forecasted passenger enplanements are distributed among international airports in the U.S. using individual airport market share factors. The airport market share analysis conducted in this step concludes that the airline business is the critical factor explaining the changes associated with airport market share. In the third and final step, the international passenger enplanements at each airport are converted to flight operations required for transporting the passengers. In this process, average load factor and average seats per aircraft are used.

The model has been integrated into the Transportation Systems Analysis Model (TSAM), a comprehensive intercity transportation planning tool. Through a simple graphic user interface implemented in the TSAM model, the user can test different future scenarios by defining a series of scaling factors for GDP, load factor and average seats per aircraft. The default values for the latter two variables are predefined in the model using 2004 historical data derived from Department of Transportation T100 international segment data.


Master of Science
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Paul, Udita. "Efficient access network selection and data demand prediction for 5G systems." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29729.

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The massive proliferation of sophisticated mobile terminals with advanced capabilities have led to an enormous surge in the demand for mobile broadband data. Also, the recent popularity of bandwidth intensive applications such as Netflix and YouTube has contributed to this demand for the wireless resources. In order to cope with this massive demand, fifth generation (5G) of wireless network is on the verge of deployment. This new generation of the wireless networks would pose different challenges for both subscribers and service providers, and the challenges need to be carefully addressed. Due to the diverse nature of the subscribers of mobile broadband, one network element is inadequate to meet the imposed requirements. Subscribers vary in terms of their usage of wireless resources as well as their preferred content. Deployment of the 5G systems promises the introduction of multiple tiers of heterogeneous networks within its architecture. This means radio access technologies (RATs) of various kinds (2G, 3G, 4G, 5G and Wi-Fi) would have to co-exist and aim to bridge the gap between the supply and demand for data. Subscribers, equipped with multi-mode or multi homing mobile terminals, can connect to one or more RATs to receive the required services. They also often run multiple applications simultaneously and as such, it must be ensured that the best access technology is assigned to a particular subscriber to maintain quality of experience and service. As such, an algorithm need to be devised that selects the best network to provide ubiquitous coverage to different types of users, running various kinds of applications, under dynamic network conditions. The network and infrastructure providers, on the other hand, face the need to meet up with the demand for data that the subscribers in different coverage regions require. In the 5G system, traditional proprietary hardware performing dedicated network functions such as packet gateway and service gateway would be replaced by softwarized virtual network functions (VNFs). These VNFs would need to be hosted in the data centres and would require computational power to process the subscribers’ traffic originating in an area. Therefore, data centres are set to play a key role in the provisioning of service in 5G systems. However, before establishing a data centre in a region, the traffic profile of that region need to be carefully studied to determine the optimal position and dimension of the facility. Furthermore, as cellular traffic differs depending on the time of the day, accurate prediction models are required to forecast future traffic demand to ensure dynamic and proper utilization of resources. This thesis aims to propose solutions to address these problems that subscribers and infrastructure providers face. Firstly, an algorithm is proposed to select the best access network for a subscriber running single or group of applications. Deviating from the existing access selection schemes in the literature, which consider the RAT-selection problem in an environment where accurate information is always available, the proposed algorithm models the problem in a completely fuzzy environment. As wireless networks are highly dynamic systems that are not only very unpredictable but also susceptible to sudden changes (for example malfunction of a particular RAT rendering it unusable), fuzzy systems are most adept in representing them. In the proposed algorithm, a new branch of fuzzy logic, Intuitionistic Fuzzy (IF) logic, is used with a popular multi-criteria decision making (MCDM) algorithm -Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to formulate a network selection problem. The IF-TOPSIS scheme is designed to accurately take in various parameters such as network conditions, different number of applications and user preferences to select the ideal network for different types of subscribers. The second part of this thesis aims to solve the problem associated with establishment of data centre and utilization of its resources. As the cellular traffic exhibit strong spatial and temporal dependencies, it becomes necessary to analyse the traffic before establishing an infrastructure like a data centre. Existing literature do not consider real world traffic while determining the best location and dimension of 5G data centres. In this thesis, a real world traffic data set is first analysed to understand the variations that are present in different regions within a city. Based on the traffic analysis, the ideal placement of the data centre is formulated as a facility location problem and solved using the Weiszfeld’s algorithm. Additionally, based on the traffic analysis, the optimal dimensions of the data centre in different regions are heuristically obtained. Finally, machine learning algorithms are employed to obtain future traffic demand values to aid dynamic allocation of data centre resources. Simulation results are presented to show the effectiveness of the proposed schemes.
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Books on the topic "Demand prediction"

1

Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. Demand Prediction in Retail. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1.

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Tomar, Anuradha, Prerna Gaur, and Xiaolong Jin, eds. Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6490-9.

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Tennant, Steven Trevor. Short term demand analysis and prediction for control of water supply. Leicester: Leicester Polytechnic, 1987.

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Tennant, S. T. Short term demand analysis and prediction for control of water supply. Leicester: Leicester Polytechnic, 1987.

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Tennant, S. T. A system description of GIDAP(Graphical Interactive Demand Analysis & Prediction program. Leicester: Leicester Polytechnic, 1986.

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Tennant, S. A system description of GIDAP: (A Graphical Interactive Demand Analysis and Prediction Program). Leicester: Leicester Polytechnic, 1986.

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Coulbeck, B. Development of a demand prediction program for use in optimal control of water supply. Leicester: Leicester Polytechnic, 1985.

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Tennant, S. Test and verification procedures for GIDAP: (A Graphical Interactive Demand Analysis and Prediction Program). Leicester: Leicester Polytechnic, 1986.

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Cronin, David. Patterns in money demand: Indicators and predictions. Dublin: Research and Publications Department, Central Bank of Ireland, 1994.

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Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms. Meets the requirements of Federal state educational standards of higher education of the last generation. For students of economic specialties, specialists, and graduate students.
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Book chapters on the topic "Demand prediction"

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Common Demand Prediction Methods." In Demand Prediction in Retail, 29–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_3.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Evaluation and Visualization." In Demand Prediction in Retail, 115–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_6.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Clustering Techniques." In Demand Prediction in Retail, 93–114. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_5.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Conclusion and Advanced Topics." In Demand Prediction in Retail, 151–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_8.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Tree-Based Methods." In Demand Prediction in Retail, 69–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_4.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Introduction." In Demand Prediction in Retail, 1–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_1.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "Data Pre-Processing and Modeling Factors." In Demand Prediction in Retail, 13–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_2.

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Cohen, Maxime C., Paul-Emile Gras, Arthur Pentecoste, and Renyu Zhang. "More Advanced Methods." In Demand Prediction in Retail, 129–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85855-1_7.

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Rubio-Bellido, Carlos, Alexis Pérez-Fargallo, and Jesús Pulido-Arcas. "Energy Demand Analysis." In Energy Optimization and Prediction in Office Buildings, 31–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90146-6_3.

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Yu, Hang, Zishuo Huang, Yiqun Pan, and Weiding Long. "Energy Demand Analysis and Prediction." In Guidelines for Community Energy Planning, 17–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9600-7_2.

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Conference papers on the topic "Demand prediction"

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Weng, Haoyuan. "Demand Prediction Model." In 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ameii-15.2015.291.

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Totamane, Raghavendra, Amit Dasgupta, Ravindra Nath Mulukutla, and Shrisha Rao. "Air cargo demand prediction." In 2009 3rd Annual IEEE Systems Conference. IEEE, 2009. http://dx.doi.org/10.1109/systems.2009.4815835.

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Ma, Rui. "A water demand prediction." In 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/amitp-16.2016.80.

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Chaver, Daniel, Luis Piñuel, Manuel Prieto, Francisco Tirado, and Michael C. Huang. "Branch prediction on demand." In the 2003 international symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/871506.871603.

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de Castro, Luciano I., and Peter Cramton. "Prediction markets for electricity demand." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483340.

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Xu, Jianfeng, Basel Abdalla, Colin Mckinnon, Annie Audibert-Hayet, Edmond Coche, and Vincent Gaffard. "Arctic Pipelines Strain Demand Prediction." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10461.

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Strain prediction is a preliminary requirement for the strain based design of arctic pipelines subject to frost heave. It involves modeling of thermal, geotechnical and mechanical of both pipe and soil behaviors. The objective is to predict frost heave and the consequences on pipeline stress-strain state. This study involves the development of three numerical models: a geothermal model to simulate the heat transfer processes in the soil; a frost heave model to simulate the coupled heat transfer and displacement of soil; and a pipe-soil interaction model to calculate the pipe stresses and strains due to soil displacement. The frost heave prediction in the present study is based on two-dimensional finite element (FE) calculations integrating the so-called “porosity rate function”. The advantage of this approach over the other models stems from a formulation consistent with continuum mechanics. The frost heave model is first validated with literature data. Then, calculations are performed to predict the heat transfer, frost heave, and pipe strain under design configurations and conditions. This study presents a good application of porosity rate function in pipeline engineering design. The developed models can be further used to investigate different design options of a chilled pipeline buried in the Arctic environment.
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Mansoor, Naseef, Md Shahriar Shamim, and Amlan Ganguly. "A Demand-Aware Predictive Dynamic Bandwidth Allocation Mechanism for Wireless Network-on-Chip." In SLIP '16: System Level Interconnect Prediction Workshop. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2947357.2947361.

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Chu, Jing, Kun Qian, Xu Wang, Lina Yao, Fu Xiao, Jianbo Li, Xin Miao, and Zheng Yang. "Passenger Demand Prediction with Cellular Footprints." In 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2018. http://dx.doi.org/10.1109/sahcn.2018.8397114.

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Dashevskiy, Mikhail, and Zhiyuan Luo. "Network Traffic Demand Prediction with Confidence." In IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference. IEEE, 2008. http://dx.doi.org/10.1109/glocom.2008.ecp.284.

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Tonchiangsai, Kanokwan, and Ganda Boonsothonsatit. "Electrical Cable Demand Prediction Using ARIMA." In 2021 10th International Conference on Industrial Technology and Management (ICITM). IEEE, 2021. http://dx.doi.org/10.1109/icitm52822.2021.00027.

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Reports on the topic "Demand prediction"

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Kimboko, Andre. A direct and behavioral travel demand model for prediction of campground use by urban recreationists. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.455.

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Sapp, James. Electricity Demand Forecasting in a Changing Regional Context: The Application of the Multiple Perspective Concept to the Prediction Process. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.574.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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Jaspersen, Johannes, Marc Ragin, and Justin Sydnor. Predicting Insurance Demand from Risk Attitudes. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26508.

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Shapovalov, Yevhenii B., Viktor B. Shapovalov, Fabian Andruszkiewicz, and Nataliia P. Volkova. Analyzing of main trends of STEM education in Ukraine using stemua.science statistics. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3883.

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STEM-education is a modern effective approach that nowadays can be interpreted in very different ways and it even has some modification (STEM/STEAM/STREAM). Anyway, the “New Ukrainian school” concept includes approaches similar to STEM-education. However, there wasn’t analyzed the current state of STEM-education in Ukraine. We propose to analyses it by using SEO analysis of one of the most popular STEM-oriented cloud environment in Ukraine stemua.science. It is proposed to use the cycle for cloud-based educational environments (publishing/SEO analysis/team’s brainstorm/prediction/creation of further plan) to improve their efficiency. It is found, that STEM-based and traditional publications are characterized by similar demand of educational process stakeholders. However, the way how teachers and students found the publication proves that traditional keywords (47.99 %) used significantly more common than STEM keywords (2.67 %). Therefore, it is proved that STEM-methods are less in demand than traditional ones. However, considering the huge positive effect of the STEM method, stemua.science cloud educational environment provides a positive effect on the educational process by including the STEM-aspects during finding traditional approaches of education by stakeholders of the educational process.
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Hunt, Will, and Jacqueline O'Reilly. Rapid Recruitment in Retail: Leveraging AI in the hiring of hourly paid frontline associates during the Covid-19 Pandemic. Digital Futures at Work Research Centre, March 2022. http://dx.doi.org/10.20919/alnb9606.

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Increased demand due to the Coronavirus pandemic created the need for Walmart to onboard tens of thousands of workers in a short period. This acted as a catalyst for Walmart to bring forward existing plans to update the hiring system for store-level hourly paid associates in its US stores. The Rapid Recruitment project sought to make hiring safer, faster, fairer and more effective by removing in-person interviews and leveraging machine learning and predictive analytics. This working paper reports on a case study of the Rapid Recruitment project involving semi-structured qualitative interviews with members of the project team and hiring staff at five US stores. The research finds that while implementation of the changes had been successful and the changes were largely valued by hiring staff, lack of awareness and confidence in some changes threatened to undermine some of the objectives of the changes. Reservations about the pre-employment assessment and the algorithm’s ability to predict quality hires led someusers reviewing more applications than perhaps necessary and potentially undermining prediction of 90-day turnover. Concerns about the ability to assess candidates over the phone meant that some users had reverted to in-person interviews, raising the riskof Covid transmission and potentially undermining the objective of removing the influence of human bias linked to appearance and other factors unrelated to performance. The impact of awareness and confidence in the changes to the hiring system are discussed in relation to the project objectives
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Pathak, Parag, and Peng Shi. How Well Do Structural Demand Models Work? Counterfactual Predictions in School Choice. Cambridge, MA: National Bureau of Economic Research, November 2017. http://dx.doi.org/10.3386/w24017.

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Wenzel, Mike. Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1096221.

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Flowe, Robert M., Mark Kasunic, and Mary M. Brown. Programmatic and Constructive Interdependence: Emerging Insights and Predictive Indicators of Development Resource Demand. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada528598.

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Muelaner, Jody Emlyn. Unsettled Issues in Electrical Demand for Automotive Electrification Pathways. SAE International, January 2021. http://dx.doi.org/10.4271/epr2021004.

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With the current state of automotive electrification, predicting which electrification pathway is likely to be the most economical over a 10- to 30-year outlook is wrought with uncertainty. The development of a range of technologies should continue, including statically charged battery electric vehicles (BEVs), fuel cell electric vehicles (FCEVs), plug-in hybrid electric vehicles (PHEVs), and EVs designed for a combination of plug-in and electric road system (ERS) supply. The most significant uncertainties are for the costs related to hydrogen supply, electrical supply, and battery life. This greatly is dependent on electrolyzers, fuel-cell costs, life spans and efficiencies, distribution and storage, and the price of renewable electricity. Green hydrogen will also be required as an industrial feedstock for difficult-to-decarbonize areas such as aviation and steel production, and for seasonal energy buffering in the grid. For ERSs, it is critical to understand how battery life will be affected by frequent cycling and the extent to which battery technology from hybrid vehicles can be applied. Unsettled Issues in Electrical Demand for Automotive Electrification Pathways dives into the most critical issues the mobility industry is facing.
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