Demand Prediction Model Research Articles

CGA-STNet: A dockless shared bicycle demand prediction model considering multiple spatial features and time periodicity

CGA-STNet: A dockless shared bicycle demand prediction model considering multiple spatial features and time periodicity

Development of an Ensemble Model for Power Demand Prediction in High-Rise Hotels with Environmental Variables

Development of an Ensemble Model for Power Demand Prediction in High-Rise Hotels with Environmental Variables

Parking demand prediction model based on entries∙exits data: A case study of a university hospital

Parking demand prediction model based on entries∙exits data: A case study of a university hospital

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to ‘how high the regularity of customers is’, filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research.

Optimizing Urban Mobility: A Comparative Analysis of Taxi Demand Prediction Models

Optimizing Urban Mobility: A Comparative Analysis of Taxi Demand Prediction Models

A Distributed VMD-BiLSTM Model for Taxi Demand Forecasting with GPS Sensor Data.

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply-demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs.

A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks.

Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN). This model predicts spatial user demand for bikes between stations by constructing a spatial demand graph, accounting for geographical influences. For precision, ST-BDP integrates an attention-based graph convolutional network for station demand graph's temporal-spatial features, and a sequential convolutional network for multi-source data (e.g., weather, time). In real dataset, experimental results show that ST-BDP has excellent performance with mean absolute error (MAE) = 1.62, mean absolute percentage error (MAPE) = 15.82%, symmetric mean absolute percentage error (SMAPE) = 16.14%, and root mean square error (RMSE) = 2.36, outperforming the baseline techniques. This highlights its predictive accuracy and potential to guide future bike-sharing policies.

Electric vehicle charging station demand prediction model deploying data slotting

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

Big Data Analysis and Information Sharing for Innovation and Entrepreneurship Education

This study delves into the transformative potential of integrating big data analysis and information sharing in innovation and entrepreneurship education. Employing a comprehensive methodology encompassing K-Means Clustering, Decision Trees, Apriori Algorithm, and Neural Networks, the research investigates student engagement patterns, influential factors, collaborative relationships, and predictive modelling within educational settings. The findings reveal significant outcomes, with K-Means achieving a clustering precision of 75%, Decision Trees demonstrating an accuracy of 82%, Apriori Algorithm uncovering frequent itemsets with 68% support, and Neural Networks achieving a notable accuracy of 90%. Drawing insights from a diverse range of literature, including studies on big data management, demand prediction models, ecological approaches to entrepreneurship education, qualitative inquiries into startup strategies, applications of ICTs in education, and the impact of virtual gaming on SMEs’ growth, the research provides a robust foundation for understanding innovation and entrepreneurship education. This study contributes to both theoretical understanding and practical implications, guiding educators and policymakers in tailoring interventions and strategies to foster an adaptive and effective educational environment.

Efficiency evaluation of electricity demand prediction models in lower-income countries: a case study of panama

Abstract Electricity plays a crucial role in the development of countries, serving as the foundation for the functioning of modern societies and as a key indicator of a nation’s level of modernization and overall strength. Predicting a country’s electricity demand provides valuable insights into the evolving patterns of electrical energy provision and usage and helps to improve the flexibility of the energy distribution network. In addition, it provides early warnings and decision support for governments, businesses, and individuals about future electricity consumption. This plays a pivotal function in optimizing power distribution, adjusting the proportion of power generation sources, and ensuring a continuous and secure electricity supply. This paper uses the example of electricity demand in Panama and uses the LSTM model and Prophet model to compare their predictions for electricity demand in lower-income countries, with the aim of improving accuracy. Furthermore, it introduces a data set representing the electricity demand of higher-income countries, using the United Kingdom as a representative, to compare the forecast results between the two countries. The results indicate that within the Panama data set, the Prophet model exhibits better accuracy than the LSTM model. On the contrary, in the data set representing higher-income countries with the United Kingdom as a representative, the LSTM model outperforms the Prophet model. This leads to the conclusion that the selection of a predictive model is specific to each country, influenced by factors such as development status, geographic location, policies, energy markets, and more. A specific model may not be universally applicable due to these unique conditions, requiring tailored-modeling approaches for precise and efficient allocation and scheduling of electricity resources. This approach ensures the improvement of residents’ living standards and quality of life while meeting the typical demands of a country’s economic and social development.

Analysis of Low-Carbon, Energy-Saving, and Emission Reduction Models Based on Rail Transit

It is difficult to define scientific applicability conditions when dealing with different evaluation objects and scope. This article is based on multi-source big data such as the Automatic Fare Collection System (AFC) of urban rail transit, comprehensively considering the multidimensional impact of urban rail transit on the urban transportation system, and conducting a quantitative analysis of the energy-saving and emission reduction effects of urban rail transit. This study adopts a traffic emission model based on specific driving forces and a traffic demand prediction model, coupling the model and data to establish an urban rail transit energy conservation and emission reduction evaluation model suitable for different urban rail transit setting scenarios. Finally, this study selected six districts in Beijing as model application cases and used a combination of RP (display preference) survey and SP (state preference) survey to complete model parameter calibration for application cases.

Research on Topology Optimization of Building Digital Supply Chain Based on Genetic Neural Network

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

Taxi Demand Method Based on SCSSA-CNN-BiLSTM

The randomness of passengers’ travel and the blindness of empty drivers seeking passengers can lead to a serious imbalance in the spatio-temporal distribution of taxi supply and demand. In order to realize the accurate prediction of taxi demand, promote a balance between taxi supply and demand, and respond to the requirements of the sustainable development of urban transportation, a travel demand prediction model based on Sparrow Search Algorithm incorporating sine-cosine and Cauchy variants (SCSSA), Convolutional Neural Network (CNN), and Bi-directional Long Short-Term Memory (BiLSTM) is proposed. The key factors affecting travel demand are identified by constructing a set of influencing factors for feature correlation analysis. In order to overcome the overfitting or underfitting phenomenon caused by the improper parameter configuration of the CNN-BiLSTM model, the SCSSA algorithm is utilized to optimize the model. By fine tuning the model parameters, the algorithm enhanced the model’s adaptability to dataset characteristics and improved the accuracy of the prediction results. Compared with CNN, LSTM, CNN- LSTM, CNN-BiLSTM, and SSA-CNN-BiLSTM models, the Root Mean Square Error is decreased by 10.77 on average.

Construction of Ensemble Learning Model for Home Appliance Demand Forecasting

Given the increasing competition among household appliance enterprises, accurately predicting household appliance demand is crucial for enterprise supply chain management and marketing. This paper proposes a combined model integrating deep learning and ensemble learning—LSTM-RF-XGBoost—to assist enterprises in identifying customer demand, thereby addressing the complexity and uncertainty of the household appliance market demand. In this study, Long Short-Term Memory Network (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are established separately. Then, the three individual algorithms are used as the base models in the first layer, with the multiple linear regression (MLR) algorithm serving as the meta-model in the second layer, merging the demand prediction model based on the hybrid model into the overall demand prediction model. This study demonstrates that the accuracy and stability of demand prediction using the LSTM–RF–XGBoost model significantly outperform traditional single models, highlighting the significant advantages of using a combined model. This research offers practical and innovative solutions for enterprises seeking rational resource allocation through demand prediction.

Study on Parking Adaptability in Urban Complexes on Top of Subways Based on Shared Parking Spaces

Urban complexes on top of subways as a function of intensive building groups, including residential, office, business, and other types nature of land use where parking time differences are obvious, can implement shared parking spaces, thereby reducing the index of parking allotment. Currently, the parking space allocation index for complexes is only a simple superposition of different land uses, resulting in an over-allocation of parking allotments, leading to a waste of land resources and a low utilization rate of parking allotments. Considering the factor of shared parking spaces, this paper conducted an in-depth analysis of the parking adaptability of urban complexes on top of subways and selected five urban complexes on top of subway stations in Wuhan to conduct a parking survey to analyze the parking demand characteristics. This study also investigated the parking behavior of parkers and analyzed the characteristics of parking behavior in urban complexes on top of subways as well as the current parking demand prediction methods and models, establishing a parking demand prediction model based on shared parking spaces and conducting an adaptability analysis. Finally, using five urban complexes in Wuhan as examples, the number of parking spaces demanded by urban complexes on top of subways in 2025 was predicted, and Wuhan Golden Harvest Fashion Plaza was used as an example to verify the feasibility and implementation ability of the theoretical and applied research in this paper.

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Application of ANN in Tourism Business Development for Demand Forecasting and Management of Tourism Headcount

With the rapid development of tourism, it is imperative to forecast tourism demand to maintain the long-term stable development of the tourism industry and make good planning for future tourism enterprises. The study uses the classical model of artificial neural network-BP neural network for tourism number demand prediction, given the problems of traditional BP neural networks, such as prematurity and poor convergence speed, this paper studies the iterative optimization of the algorithm of particle swarm fusion immune mechanism and finds out the optimal network parameters, to build an IAPSO-BP tourism demand prediction model. Tourist amounts from 2007 to 2017 in certain area-related data samples, the training model of iterative speed and fitting effect, and the rolling forecasting method will be used to predict the 2018-2022 years of travel. It can be seen from the convergence curve that the convergence speed of parameter optimization of the IAPSO algorithm is the fastest; the improvedIAPSO-BP network has the best training fitting effect, with a relative average error of 2.03% and an absolute average error of 4.37%, which is better than other forecasting methods. The IAPSO-BP prediction model has higher accuracy and better performance, which can provide an effective basis for the development planning of tourism enterprises and has higher practical application value.

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

This paper aims to explore the design and implementation of product demand forecasting and personalized recommendation system based on data analysis. Through the collection and analysis of a large number of user behavior data, an accurate demand prediction model is constructed, and based on this, a personalized recommendation algorithm is implemented. The paper first introduces the research background and purpose, and then elaborates the research method, process and results. The method based on data analysis can effectively predict the product demand and provide users with personalized recommendation services, so as to improve user satisfaction and market competitiveness of enterprises.

A prior-knowledge-based time series model for heat demand prediction of district heating systems

Time series prediction methods are effective for predicting energy demands of district heating systems. Due to the thermal inertia of systems, historical heat load is usually applied as the most crucial input for prediction. However, it results in lateral errors (phase lags) between actual and predicted heat demand. To address this issue, a hybrid time series model is proposed based on autoregressive integrated moving average model and resistance–capacitance model. To reduce the lateral errors, estimated current and future heat demands are introduced as exogenous inputs of the autoregressive integrated moving average model, and they perform as prior knowledge. To generate estimated heat demand, a piecewise resistance–capacitance model is proposed, which considers various climatic and operating conditions. Lateral error of the hybrid model is decreased by 7.1% compared with conventional autoregressive integrated moving average model. Moreover, the hybrid model retains good post-hoc interpretability, which proves that exogenous inputs can effectively reduce lateral errors.

Origin-destination demand prediction of public transit using graph convolutional neural network

The insight into origin–destination (OD) demand patterns aids transport planners in making the public transit system more efficient and attractive. This may encourage individuals to shift from private vehicles to public transit, easing the burden on traffic and its negative impacts. Hence, to know how OD demand is going to vary in future, a state-of-the-art OD demand prediction model needs to be developed. Previously, studies have developed zone-based prediction models which may not be appropriate for predicting OD demand within a route of public transit. Additionally, spatial correlations between the stops of public transit must be included in the model for improved forecasting accuracy. Hence, in an effort to fulfil these gaps, a Graph Convolutional Neural Network (GCN) is developed to forecast the OD demand of public bus transit with nodes being the bus stops and links between them representing the passenger flow between the stops. Land use around the bus stops is retrieved as a node feature and included in the model to account for the spatial correlation between the stops. The model is trained using a real-life dataset from the public bus service of Davangere city located in India. Land use around the bus stops is extracted from the Davangere city master plan, procured from the urban development authority. The developed model is compared with conventional models and the findings show that the GCN model performs better in terms of prediction accuracy than the baseline models. Additionally, at the stop level, the performance of the model remained stable due to the inclusion of land use data compared to conventional models where land use data was not considered.