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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Tang, Li Fang. "CPSO-SVM Based Petroleum Demand Prediction." Applied Mechanics and Materials 273 (January 2013): 91–96. http://dx.doi.org/10.4028/www.scientific.net/amm.273.91.
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Study of oil demand, oil demand uncertainty, leading to its strong non-linear, sudden change characteristic, causes the linear modeling of traditional method and neural network prediction precision is low. In order to accurately forecast demand, presents a chaos particle swarm optimization of support vector machine oil demand forecasting method (CPSO-SVM). The CPSO SVM parameter optimization, and then using SVM to petroleum demand nonlinear variation modeling, finally to 1989~ 2007 oil demand data for simulation, the results show that, compared with other oil demand forecast algorithm, CPSO-SVM raised oil demand forecast accuracy, as demand for oil to provide a new method for predicting.
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Kim, Eunhye, Hani S. Mahmassani, Haleh Ale-Ahmad, and Marija Ostojic. "Day-to-Day Learning Framework for Online Origin–Destination Demand Estimation and Network State Prediction." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 11, 2019): 195–208. http://dx.doi.org/10.1177/0361198119852075.
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Origin–destination (O–D) demand is a critical component in both online and offline dynamic traffic assignment (DTA) systems. Recent advances in real-time DTA applications in large networks call for robust and efficient methodologies for online O–D demand estimation and prediction. This study presents a day-to-day learning framework for a priori O–D demand, along with a predictive data-driven O–D correction approach for online consistency between predicted and observed (sensor) values. When deviations between simulation and real world are observed, a consistency-checking module initiates O–D demand correction for the given prediction horizon. Two predictive correction methods are suggested: 1) simple gradient method, and 2) Taylor approximation method. New O–D demand matrices, corrected for 24 simulation hours by the correction module, are used as the updated a priori demand for the next day simulation. The methodology is tested in a real-world network, Kansas City, MO, for a 3-day period. Actual tests in real-world networks of online DTA systems have been very limited in the literature and in actual practice. The test results are analyzed in time and space dimensions. The overall performance of observed links is assessed. To measure the impact of O–D correction and daily O–D updates, traffic prediction performance with the new modules is compared with the base case. Predictive O–D correction improves prediction performance in a long prediction window. Also, daily updated O–D demand provides better initial states for traffic prediction, enhancing prediction in short prediction windows. The two modules collectively improve traffic prediction performance of the real-time DTA system.
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Ramana, Dr A. Venkata. "Taxi Demand Prediction using ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3811–15. http://dx.doi.org/10.22214/ijraset.2022.43912.
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Abstract: Taxi plays a crucial role in transportation especially in urban areas.Predicting the future demand for taxis in particular geographical location will greatly help internet based transportation companies like Ola, Uber etc. So that we can drastically decrease the waiting time of customers/passengers and also it helps taxi drivers to move to particular location where demand is high eventually making passengers,drivers and companies happy. In this Project we like to predict the demand for taxi in particular location for next 10 min using previous time series data .we want to perform this task of regression using machine learning models with high accuracy and then we would like to apply deep learning models and compare the results.we like to propose the best suited and high accuracy model for the problem.It will greatly help companies in managing the taxi fleet in cities.
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Sachdeva, Purnima, and K. N. Sarvanan. "Prediction of Bike Sharing Demand." Oriental journal of computer science and technology 10, no. 1 (March 21, 2017): 219–26. http://dx.doi.org/10.13005/ojcst/10.01.30.
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Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with zest. Even developing countries like India are adopting the trend with a bike sharing system in the pipeline for Karnataka. This paper tackles the problem of predicting the number of bikes which will be rented at any given hour in a given city, henceforth referred to as the problem of ‘Bike Sharing Demand’. In this vein, this paper investigates the efficacy of standard machine learning techniques namely SVM, Regression, Random Forests, Boosting by implementing and analyzing their performance with respect to each other.This paper also presents two novel methods, Linear Combination and Discriminating Linear Combination, for the ‘Bike Sharing Demand’ problem which supersede the aforementioned techniques as good estimates in the real world.
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Mohammadi, Milad, Song Han, Tor M. Aamodt, and William J. Dally. "On-Demand Dynamic Branch Prediction." IEEE Computer Architecture Letters 14, no. 1 (January 1, 2015): 50–53. http://dx.doi.org/10.1109/lca.2014.2330820.
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Jiang, Aiping, Junjun Gao, Ying Wan, Xinyi Zhao, and Siqi Shan. "Intermittent Prediction Method Based On Marcov Method And Grey Prediction Method." European Scientific Journal, ESJ 12, no. 15 (May 30, 2016): 81. http://dx.doi.org/10.19044/esj.2016.v12n15p81.
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This paper concentrates on the intermittent demand for electric power supply and studies the method of demand prediction. This chapter first divides the demand for electric power supply into two statistical sequences: (1) sequence of demand occurrence, among which “1”stands for the occurrence of demand,“0”means that the demand fails to occur; (2) sequence of demand quantity. Next the author predicts the moment of time and the number of times n that demand occurs within a specific time interval in the future based on 0-1 sequence using Markov arrival process (MAP). Then the paper forecasts the demand quantity in subsequent n intervals using Grey prediction model GM (1, 1) based on the sequence of demand quantity. Finally, the author places the demand quantity in the n intervals in order at the moments where demand occurs to get the predicted result of demand for electric material with intermittent demand. According to instance analysis, the integrated approach mentioned in this paper surpasses existing methods in providing accurate prediction on data of product with intermittent demand.
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Shuang, Qing, and Rui Ting Zhao. "Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China." Water 13, no. 3 (January 27, 2021): 310. http://dx.doi.org/10.3390/w13030310.
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Predicting water demand helps decision-makers allocate regional water resources efficiently, thereby preventing water waste and shortage. The aim of this study is to predict water demand in the Beijing–Tianjin–Hebei region of North China. The explanatory variables associated with economy, community, water use, and resource availability were identified. Eleven statistical and machine learning models were built, which used data covering the 2004–2019 period. Interpolation and extrapolation scenarios were conducted to find the most suitable predictive model. The results suggest that the gradient boosting decision tree (GBDT) model demonstrates the best prediction performance in the two scenarios. The model was further tested for three other regions in China, and its robustness was validated. The water demand in 2020–2021 was provided. The results show that the identified explanatory variables were effective in water demand prediction. The machine learning models outperformed the statistical models, with the ensemble models being superior to the single predictor models. The best predictive model can also be applied to other regions to help forecast water demand to ensure sustainable water resource management.
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Geng, Shaoqing, and Hanping Hou. "Demand Stratification and Prediction of Evacuees after Earthquakes." Sustainability 13, no. 16 (August 7, 2021): 8837. http://dx.doi.org/10.3390/su13168837.
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In recent years, frequent natural disasters have brought huge losses to human lives and property, directly affecting social stability and economic development. Since the driving factor of disaster management operations is speed, it will face severe challenges and tremendous pressure when matching the supply of emergency resources with the demand. However, it is difficult to figure out the demands of the affected area until the initial post-disaster assessment is completed and demand is constantly changing. The focus of this paper is to stratify the evacuation needs and predict the number of evacuees and supplies demanded after an earthquake. This research takes a large-scale earthquake as an example to analyze the characteristics of evacuation demand stratification and the factors that affect the demands of evacuees. The forecast model for the number of evacuees is selected and improved. Moreover, combining the influencing factors of materials demand and the number of evacuees, a forecast model of materials demand for evacuees is constructed. The proposed model is used in the case of the Ya’an earthquake in China to estimate the number of evacuees and the daily need for emergency supplies.
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Sheha, Moataz, and Kody Powell. "Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques." Processes 7, no. 12 (November 21, 2019): 870. http://dx.doi.org/10.3390/pr7120870.
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With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.
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Liu, Hao, Qiyu Wu, Fuzhen Zhuang, Xinjiang Lu, Dejing Dou, and Hui Xiong. "Community-Aware Multi-Task Transportation Demand Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 320–27. http://dx.doi.org/10.1609/aaai.v35i1.16107.
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Transportation demand prediction is of great importance to urban governance and has become an essential function in many online applications. While many efforts have been made for regional transportation demand prediction, predicting the diversified transportation demand for different communities (e.g., the aged, the juveniles) remains an unexplored problem. However, this task is challenging because of the joint influence of spatio-temporal correlation among regions and implicit correlation among different communities. To this end, in this paper, we propose the Multi-task Spatio-Temporal Network with Mutually-supervised Adaptive task grouping (Ada-MSTNet) for community-aware transportation demand prediction. Specifically, we first construct a sequence of multi-view graphs from both spatial and community perspectives, and devise a spatio-temporal neural network to simultaneously capture the sophisticated correlations between regions and communities, respectively. Then, we propose an adaptively clustered multi-task learning module, where the prediction of each region-community specific transportation demand is regarded as distinct task. Moreover, a mutually supervised adaptive task grouping strategy is introduced to softly cluster each task into different task groups, by leveraging the supervision signal from one another graph view. In such a way, Ada-MSTNet is not only able to share common knowledge among highly related communities and regions, but also shield the noise from unrelated tasks in an end-to-end fashion. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our approach compared with seven baselines.
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P, Loganathan. "Cloud based Monitoring and Control Automation of Industrial Demand Prediction System." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1808–16. http://dx.doi.org/10.5373/jardcs/v12sp7/20202293.
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Wan, Kun Yang. "Research on Urban Water Demand Prediction." Advanced Materials Research 594-597 (November 2012): 2037–40. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.2037.
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Water demand prediction adopts combined prediction method based on BP neural network prediction model, grey G (1,1) prediction model, time sequence prediction model (second multinomial exponential smoothing model) and single linear regression model (Cubics Ratio model). Empirical results show that combined prediction method makes comprehensive use of information of every separate prediction model, and thus enhances prediction accuracy.
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Pérez, Fernando A. Acosta, Gabriel E. Rodríguez Ortiz, Everson Rodríguez Muñiz, Fernando J. Ortiz Sacarello, Jee Eun Kang, and Daniel Rodriguez-Roman. "Predicting Trip Cancellations and No-Shows in Paratransit Operations." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (June 12, 2020): 774–84. http://dx.doi.org/10.1177/0361198120924661.
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The productivity of paratransit systems could be improved if transit agencies had the tools to accurately predict which trip reservations are likely to result in trips. A potentially useful approach to this prediction task is the use of machine learning algorithms, which are routinely applied in, for example, the airline and hotel industries to make predictions on reservation outcomes. In this study, the application of machine learning (ML) algorithms is examined for two prediction problems that are of interest to paratransit operations. In the first problem the operator is only concerned with predicting which reservations will result in trips and which ones will not, while in the second prediction problem the operator is interested in more than two reservation outcomes. Logistic regression, random forest, gradient boosting, and extreme gradient boosting were the main machine learning algorithms applied in this study. In addition, a clustering-based approach was developed to assign outcome probabilities to trip reservations. Using trip reservation data provided by the Metropolitan Bus Authority of Puerto Rico, tests were conducted to examine the predictive accuracy of the selected algorithms. The gradient boosting and extreme gradient boosting algorithms were the best performing methods in the classification tests. In addition, to illustrate an application of the algorithms, demand forecasting models were generated and shown to be a promising approach for predicting daily trips in paratransit systems. The best performing method in this exercise was a regression model that optimally combined the demand predictions generated by the machine learning algorithms considered in this study.
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Lestuzzi, Pierino, and Lorenzo Diana. "Accuracy Assessment of Nonlinear Seismic Displacement Demand Predicted by Simplified Methods for the Plateau Range of Design Response Spectra." Advances in Civil Engineering 2019 (September 19, 2019): 1–16. http://dx.doi.org/10.1155/2019/1396019.
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The nonlinear seismic displacement demand prediction for low-period structures, i.e., with an initial fundamental period situated in the plateau of design response spectra, is studied. In Eurocode 8, the computation of seismic displacement demands is essentially based on a simplified method called the N2 method. Alternative approaches using linear computation with increased damping ratio are common in other parts of the world. The accuracy of three methods for seismic displacement demand prediction is carefully examined for the plateau range of Type-1 soil class response spectra of Eurocode 8. The accuracy is assessed through comparing the displacement demand computed using nonlinear time-history analysis (NLTHA) with predictions using simplified methods. The N2 method, a recently proposed optimization of the N2 method, and the Lin and Miranda method are compared. Nonlinear single-degree-of-freedom systems are subjected to several sets of recorded earthquakes that are modified to match design response spectra prescribed by Eurocode 8. The shape of Eurocode 8 response spectra after the plateau is defined by a constant pseudovelocity range (1/T). However, the slope of this declining branch may be specified using precise spectral microzonation investigation. However, the N2 method has been found to be particularly inaccurate with certain microzonation response spectra that are characterized by a gently decreasing branch after the plateau. The present study investigates the impact of the slope of the decreasing branch after the plateau of response spectra on the accuracy of displacement demand predictions. The results show that the accuracy domain of the N2 method is restricted to strength reduction factor values around 3.5. Using the N2 method to predict displacement demands leads to significant overestimations for strength reduction factors smaller than 2.5 and to significant underestimations for strength reduction factors larger than 4. Fortunately, the optimized N2 method leads to accurate results for the whole range of strength reduction factors. For small values of strength reduction factors, up to 2.5, the optimized N2 method and the Lin and Miranda method both provide accurate displacement demand predictions. However, the accuracy of displacement demand prediction strongly depends on the shape of the response spectrum after the plateau. A gently decreasing branch after the plateau affects the accuracy of displacement demand predictions. A threshold value of 0.75 for the exponent of the decreasing branch (1/Tα) after the plateau is proposed. This issue should be considered for the ongoing developments of Eurocode 8.
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Xue, Xiang Hong, Xiao Feng Xue, and Lei Xu. "Study on Improved PCA-SVM Model for Water Demand Prediction." Advanced Materials Research 591-593 (November 2012): 1320–24. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1320.
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construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency. Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices, thus to reduce SVM input dimensions; besides, it also introduces genetic algorithm, solved the problem that the traditional SUV parameters cannot optimized dynamically. A simulated experiment proves that the predication accuracy of this model is higher than SVM, BP neural network; this model has higher generalization ability and is an effective model for predicting water demand.
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Jiajing, Jiang, Cui Qingan, and Zhu Aoquan. "Research on Gold Demand Prediciton Based on GM-GPR Model." E3S Web of Conferences 253 (2021): 02014. http://dx.doi.org/10.1051/e3sconf/202125302014.
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The prediction system of gold demands in China is faced with issues such as uncertain factors, limited historical data, and nonlinearity. In order to have a more accurate prediction of gold demands, a prediction method based on the integration of grey prediction and Gaussian process regression is proposed. Specifically, equal weights are assigned to each model and a grey prediction is adopted to reflect the uncertain and changing relationship of gold demands, with Gaussian process regression indicating the nonlinear impacts of factors on gold demands. Moreover, modified particle swarm optimization plays a role in optimizing the hyper-parameters of Gaussian process regression, which solves the issue that conjugate gradient algorithms depend on initial value setting and are susceptible to be confined by locally optimal solutions. According to the study, the proposal of the paper is superior to a separate Gaussian process regression or grey prediction in terms of better predicting gold demands.
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Lin, Adrian Xi, Andrew Fu Wah Ho, Kang Hao Cheong, Zengxiang Li, Wentong Cai, Marcel Lucas Chee, Yih Yng Ng, Xiaokui Xiao, and Marcus Eng Hock Ong. "Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction." International Journal of Environmental Research and Public Health 17, no. 11 (June 11, 2020): 4179. http://dx.doi.org/10.3390/ijerph17114179.
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The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.
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Tang, Zhongjun, and Lang Ni. "An Interval Reliability Demand Prediction Method Combined with XGBoost and D-S Evidence Theory in Film Preparation Period." Journal of Physics: Conference Series 2025, no. 1 (September 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/2025/1/012022.
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Abstract The lack of historical sales data and word-of-mouth information in the film preparation period, the few available variables and the uncertainty in the prediction process lead to the difficulty in predicting the total box office demand of films. To solve this problem, this paper constructed and verified the prediction method of interval reliability demand in the film preparation period, which combined XGBoost algorithm and D-S evidence theory. Firstly, the total box office interval was effectively divided according to the sample data of the training set, and XGBoost was used to complete the calculation of the reliability function value of the evidence variables. Then, the D-S evidence theory was used for information fusion to obtain the results of box office interval reliability fusion. Finally, the box office attribution was judged by the interval reliability, so as to realize the interval reliability demand prediction in the preparatory period. The validity of the proposed method was verified by selecting the data of Chinese films from 2017 to 2019, and it was compared with the classical predictive classification algorithm. The results showed that the method has higher prediction accuracy and better generalization ability.
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Almaghrebi, Ahmad, Fares Aljuheshi, Mostafa Rafaie, Kevin James, and Mahmoud Alahmad. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods." Energies 13, no. 16 (August 16, 2020): 4231. http://dx.doi.org/10.3390/en13164231.
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Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration.
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Kattimani, Sourabh. "Water Demand Prediction using KNN Algorthim." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4705–14. http://dx.doi.org/10.22214/ijraset.2022.45010.
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Abstract: Many factors influence irrigation water requirement in an agriculture field. those factors are age of plant, type of soil, temperature, level of sunlight, water needed. Many factors influence irrigation water requirement in an agriculture field. those factors are age of plant, type of soil, temperature, level of sunlight, water needed. Despite the multiple solution proposed, still the quantity of water overflood and underfloor in the agriculture felid. The artificial influence on irrigation requirement should be thought of an important impact factor, considering the requirement of water, the technology can help in preserving large quantity of water in agriculture felid. development of complex and elaborate forecasting methods such as artificial neural network (ANN)can be costly to develop and implement with the limited recourses available The balance between water supply and demand requires efficient water supply system management techniques. This Despite the multiple solution proposed, still the quantity of water overflood and underfloor in the agriculture felid. The artificial influence on irrigation requirement should be thought of an important impact factor, considering the requirement of water, the technology can help in preserving large quantity of water in agriculture felid. development of complex and elaborate forecasting methods such as artificial neural network (ANN)canbe costly to develop and implement with the limited recourses available
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Banjac, G., M. Vašak, and M. Baotić. "Adaptable urban water demand prediction system." Water Supply 15, no. 5 (April 22, 2015): 958–64. http://dx.doi.org/10.2166/ws.2015.048.
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In this work, identification of 24-hours-ahead water demand prediction model based on historical water demand data is considered. As part of the identification procedure, the input variable selection algorithm based on partial mutual information is implemented. It is shown that meteorological data on a daily basis are not relevant for the water demand prediction in the sense of partial mutual information for the analysed water distribution systems of the cities of Tavira, Algarve, Portugal and Evanton East, Scotland, UK. Water demand prediction system is modelled using artificial neural networks, which offer a great potential for the identification of complex dynamic systems. The adaptive tuning procedure of model parameters is also developed in order to enable the model to adapt to changes in the system. A significant improvement of the prediction ability of such a model in relation to the model with fixed parameters is shown when a certain trend is present in the water demand profile.
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Tolfrey, Keith. "Prediction Of Aerobic Demand In Children." Medicine & Science in Sports & Exercise 37, Supplement (May 2005): S17—S18. http://dx.doi.org/10.1249/00005768-200505001-00120.
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Tolfrey, Keith. "Prediction Of Aerobic Demand In Children." Medicine & Science in Sports & Exercise 37, Supplement (May 2005): S17???S18. http://dx.doi.org/10.1097/00005768-200505001-00120.
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Rothenhoefer, Kathryn M., and William R. Stauffer. "Dopamine prediction error responses update demand." Proceedings of the National Academy of Sciences 114, no. 52 (December 12, 2017): 13597–99. http://dx.doi.org/10.1073/pnas.1718818115.
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Al-Anbuky, A., S. Bataineh, and S. Al-Aqtash. "Power demand prediction using fuzzy logic." Control Engineering Practice 3, no. 9 (September 1995): 1291–98. http://dx.doi.org/10.1016/0967-0661(95)00128-h.
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Totamane, Raghavendra, Amit Dasgupta, and Shrisha Rao. "Air Cargo Demand Modeling and Prediction." IEEE Systems Journal 8, no. 1 (March 2014): 52–62. http://dx.doi.org/10.1109/jsyst.2012.2218511.
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Srinivasan, Dipti. "Energy demand prediction using GMDH networks." Neurocomputing 72, no. 1-3 (December 2008): 625–29. http://dx.doi.org/10.1016/j.neucom.2008.08.006.
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Chang, Han wen, Yu chin Tai, and Jane Yung jen Hsu. "Context-aware taxi demand hotspots prediction." International Journal of Business Intelligence and Data Mining 5, no. 1 (2010): 3. http://dx.doi.org/10.1504/ijbidm.2010.030296.
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Patil, Rohit, Priyadarshani Alandikar, Vaibhav Chaudhari, Pradnya Patil, and Prof Swarupa Deshpande. "Water Demand Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 122–28. http://dx.doi.org/10.22214/ijraset.2022.47797.
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bstract: Water is of paramount importance for the existence of life on Earth. The causes of water depletion are both natural and anthropogenic. On Earth, the amount of freshwater has remained persistent over span but the population has mushroomed. Therefore, striving for freshwater intensifies day by day. Proper management and forecasting are required for better and effective water usage plans. Water demand and population forecasting are the major parameters for an Urban Water Management. Machine learning is among the best-known techniques for such forecasting. Machine learning is a data analytics technique that provides machines the potential to learn without being comprehensively programmed. Unlike the traditional methods of demand forecasting that were not suitable for historical unstructured and semi structured data, machine learning takes into account or has the capabilities for analyzing such data.
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Gupta, Ashish, and Rishabh Mehrotra. "Joint Attention Neural Model for Demand Prediction in Online Marketplaces." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5170.
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As an increasing number of consumers rely on online marketplaces to purchase goods from, demand prediction becomes an important problem for suppliers to inform their pricing and inventory management decisions. Business volatility and the complexity of factors influence demand, which makes it a harder quantity to predict. In this paper, we consider the case of an online classified marketplace and propose a joint multi-modal neural model for demand prediction. The proposed neural model incorporates a number of factors including product description information (title, description, images), contextual information (geography, similar products) and historic interest to predict demand. Large-scale experiments on real-world data demonstrate superior performance over established baselines. Our experiments highlight the importance of considering, quantifying and leveraging the textual content of products and image quality for enhanced demand prediction. Finally, we quantify the impact of the different factors in predicting demand.
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VAN VAERENBERGH, STEVEN, ALBERTO SALCINES MENEZO, and OSCAR COSIDO COBOS. "DEVELOPMENT OF A SHORT-TERM PREDICTION SYSTEM FOR ELECTRICITY DEMAND." DYNA 96, no. 3 (May 1, 2021): 285–89. http://dx.doi.org/10.6036/9894.
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This article describes the development of a prediction method for the demand for electrical energy of a marketer's customer portfolio. The project is motivated by the economic benefit produced when the entity has accurate estimates of energy demand when buying energy in an electricity auction. The developed system is based on time series analysis and machine learning. As this system was part of a real-world project with data from a real environment, the article focuses on practical aspects of the design and development of system of these characteristics, such as the heterogeneity of data sources, and the delay in data availability. The predictions obtained by the developed system are compared with the results of a simple method used in practice. Keywords: energy demand prediction, electric power, machine learning, data-driven prediction.
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Asiah, Mat, Khidzir Nik Zulkarnaen, Deris Safaai, Mat Yaacob Nik Nurul Hafzan, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "A Review on Predictive Modeling Technique for Student Academic Performance Monitoring." MATEC Web of Conferences 255 (2019): 03004. http://dx.doi.org/10.1051/matecconf/201925503004.
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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous researchers to develop the best performance model using variety of students data, techniques, algorithms and tools. Predictive modeling used in predicting student performance are related to several learning tasks such as classification, regression and clustering. To achieve best prediction model, a lot of variables have been chosen and tested to find most influential attributes to perform prediction. Accurate performance prediction will be helpful in order to provide guidance in learning process that will benefit to students in avoiding poor scores. The predictive model furthermore can help instructor to forecast course completion including student final grade which are directly correlated to student performance success. To harvest an effective predictive model, it requires a good input data and variables, suitable predictive method as well as powerful and robust prediction model.
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Phithakkitnukooon, Santi, Karn Patanukhom, and Merkebe Getachew Demissie. "Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional Network." ISPRS International Journal of Geo-Information 10, no. 11 (November 13, 2021): 773. http://dx.doi.org/10.3390/ijgi10110773.
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Dockless electric scooters (e-scooter) have emerged as a green alternative to automobiles and a solution to the first- and last-mile problems. Demand anticipation, or being able to accurately predict spatiotemporal demand of e-scooter usage, is one supply–demand balancing strategy. In this paper, we present a dockless e-scooter demand prediction model based on a fully convolutional network (FCN) coupled with a masking process and a weighted loss function, namely, masked FCN (or MFCN). The MFCN model handles the sparse e-scooter usage data with its masking process and weighted loss function. The model is trained with highly correlated features through our feature selection process. Next-hour and next 24-h prediction schemes have been tested for both pick-up and drop-off demands. Overall, the proposed MFCN outperforms other baseline models including a naïve forecasting, linear regression, and convolutional long short-term memory networks with mean absolute errors of 0.0434 and 0.0464 for the next-hour pick-up and drop-off demand prediction, respectively, and the errors of 0.0491 and 0.0501 for the next 24-h pick-up and drop-off demand prediction, respectively. The developed MFCN expands the collection of deep learning techniques that can be applied in the transportation domain, especially spatiotemporal demand prediction.
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Zhu, Ya Hong, Ji Ping Cao, Wen Xia Sun, Yang Tao Fan, and Zhi Hui Zhao. "Demand Forecasting Model Based on Equipment Maintenance Resources in Virtual Warehousing." Applied Mechanics and Materials 556-562 (May 2014): 5442–49. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5442.
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Based on the theory of virtual warehousing, the optimization system for equipment maintenance resources in virtual warehousing is established for the security task of equipment maintenance resources. According to the prediction problems on the spare parts requirements for equipment maintenance in this system, the demand forecasting model, based on the combination of rough sets and grey prediction, is adopted. The results of simulation experiment show that this method applied in equipment maintenance spare resources prediction is reliable and with accurate information. While, the relative error and absolute error of the predictive value and practical value are very small, which shows the prediction model is of high precision for the accurate effect prediction. As a result, this model and algorithum is proved to be effective to provide theoretical and practical support for equipment maintenance spare resources in information warfare.
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Kim, Jin-Young, and Sung-Bae Cho. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder." Energies 12, no. 4 (February 22, 2019): 739. http://dx.doi.org/10.3390/en12040739.
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As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 minutes with 60-minute demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.
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Gwaivangmin, BI, and JD Jiya. "WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR SUPERVISORY CONTROL." Nigerian Journal of Technology 36, no. 1 (December 29, 2016): 148–54. http://dx.doi.org/10.4314/njt.v36i1.19.
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With increase in population growth, industrial development and economic activities over the years, water demand could not be met in a water distribution network. Thus, water demand forecasting becomes necessary at the demand nodes. This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos. The proposed model will be developed based on historic records of water demand in the 15 selected demand nodes for 60 days, 24 hours run. The data set is categorized into two set, one for training the neural network and the other for testing, with a learning rate of 50 and hidden nodes of 10 of the neural network model. The prediction results revealed a satisfactory performance of the neural network prediction of the water demand. The predictions are then used for supervisory control to remotely control and monitor the hydraulic parameters of the water demand nodes. The practical application in the plant will cut down the cost of water production and even to a large extend provide optimal operation of the distribution networks solving the perennial problem of water scarcity in Jos. http://dx.doi.org/10.4314/njt.v36i1.19
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Bo, Qiuyu, and Wuqun Cheng. "Intelligent Control of Agricultural Irrigation through Water Demand Prediction Based on Artificial Neural Network." Computational Intelligence and Neuroscience 2021 (November 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/7414949.
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In irrigated areas, the intelligent management and scientific decision-making of agricultural irrigation are premised on the accurate estimation of the ecological water demand for different crops under different spatiotemporal conditions. However, the existing estimation methods are blind, slow, or inaccurate, compared with the index values of the water demand collected in real time from irrigated areas. To solve the problem, this paper innovatively introduces the spatiotemporal features of ecological water demand to the forecast of future water demand by integrating an artificial neural network (ANN) for water demand prediction with the prediction indices of water demand. Firstly, the ecological water demand for agricultural irrigation of crops was calculated, and a radial basis function neural network (RBFNN) was constructed for predicting the water demand of agricultural irrigation. On this basis, an intelligent control strategy was presented for agricultural irrigation based on water demand prediction. The structure of the intelligent control system was fully clarified, and the main program was designed in detail. The proposed model was proved effective through experiments.
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Zhang, Shuichao, Zhuping Zhou, Haiming Hao, and Jibiao Zhou. "Prediction model of demand for public bicycle rental based on land use." Advances in Mechanical Engineering 10, no. 12 (December 2018): 168781401881897. http://dx.doi.org/10.1177/1687814018818977.
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Land use is a primary factor affecting the demand for public bicycle rentals. Demand for public bicycle rentals during different periods of time were predicted using the following procedures. First, walking distances from the rental stations where riders returned the public bicycles to the final destinations were obtained by field investigation, and the 85th percentile statistical values were used as the scopes of influence of those stations. Then, a relationship model among the rental demands for public bicycles and the features of land use inside the influence scope of the rental station was established based on a linear regression model. Finally, considering the public bicycle system in the old urban region of Zhenhai in Ningbo city, the newly established prediction model for rental demand was tested. Results show that the model can predict the daily rental demand, rental demand during the morning peak, returns during the morning peak, rental demands during the evening peak, and returns during the evening peak. The demand prediction model can provide a significant theoretical basis for preparing the layout stations, operation and management strategies, and vehicle scheduling in the public bicycle system.
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Wang, Chunxia, Jun Bi, Qiuyue Sai, and Zun Yuan. "Analysis and Prediction of Carsharing Demand Based on Data Mining Methods." Algorithms 14, no. 6 (June 5, 2021): 179. http://dx.doi.org/10.3390/a14060179.
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With the development of the sharing economy, carsharing is a major achievement in the current mode of transportation in sharing economies. Carsharing can effectively alleviate traffic congestion and reduce the travel cost of residents. However, due to the randomness of users’ travel demand, carsharing operators are faced with problems, such as imbalance in vehicle demand at stations. Therefore, scientific prediction of users’ travel demand is important to ensure the efficient operation of carsharing. The main purpose of this study is to use gradient boosting decision tree to predict the travel demand of station-based carsharing users. The case study is conducted in Lanzhou City, Gansu Province, China. To improve the accuracy, gradient boosting decision tree is designed to predict the demands of users at different stations at various times based on the actual operating data of carsharing. The prediction results are compared with results of the autoregressive integrated moving average. The conclusion shows that gradient boosting decision tree has higher prediction accuracy. This study can provide a reference value for user demand prediction in practical application.
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Xu, Xiaomei, Zhirui Ye, Jin Li, and Mingtao Xu. "Understanding the Usage Patterns of Bicycle-Sharing Systems to Predict Users’ Demand: A Case Study in Wenzhou, China." Computational Intelligence and Neuroscience 2018 (September 5, 2018): 1–21. http://dx.doi.org/10.1155/2018/9892134.
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Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based aspect and station-based aspect to provide some guidance for users’ demand prediction. After that, the methodology combining cluster analysis, a back-propagation neural network (BPNN), and comparative analysis is proposed to predict users’ demand. Cluster analysis is used to identify different service types of stations, the BPNN method is utilized to establish the demand prediction models for different service types of stations, and comparative analysis is employed to determine if the accuracy of the prediction models is improved by making a distinction among stations and working/nonworking days. Finally, a case study is conducted to evaluate the performance of the proposed methodology. Results indicate that making a distinction among stations and working/nonworking days when predicting users’ demand can improve the accuracy of prediction models.