Demand Estimation Model Research Articles

Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data

This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the air transport industry. The present paper focuses on the development of a model for medium-term flight demand estimation by flight destinations. This is based on the analysis of historical airline data on dates, departure times, and passenger demand, as well as the consideration of the influence of macroeconomic indicators, namely gross regional product (GRP), median per capita income, and population of departure and arrival points. This paper reviews international experience in the development of demand forecasting models and their use for resource planning in the industry. The developed model was evaluated using historical data on demand for a single turnaround flight operated by an airline. The developed model allows for the forecasting of the distribution of potential demand for airline flight destinations in the medium term, utilizing comprehensive historical data on departure times and flight demand by destination.

Estimation of demand models for long-distance international travel – Key determinants for Swedes’ travel abroad

Estimation of demand models for long-distance international travel – Key determinants for Swedes’ travel abroad

Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response

This study aims to design an efficient hybrid solar-wind fast charging station with an energy storage system (ESS) to maximize station efficiency and reduce grid dependence. The research employs Monte Carlo simulation to capture renewable energy source (RES) uncertainties, models stochastic electric vehicles (EVs) driver behavior and fleet diversity, and utilizes an Erlang B queuing model for EV load demand estimation. A hybrid optimization approach combining the Binary-Gravitational Search Optimization Algorithm (B-GOA) and the Non-dominated Crowding Sort Optimization Algorithm (NCSOA) is implemented for efficient optimization in a combined binary-continuous solution space. Results demonstrate superior performance of the proposed approach compared to existing methods, with high-RES penetration significantly reducing grid reliance. This study contributes to advancing sustainable EV charging infrastructure development and enhancing overall grid stability through improved load flexibility and demand response management. The analysis reveals Scenario IV as the most economically viable, with the highest Net Present Value of 1,025,895.32€. Most scenarios favor 5 chargers (44–46 kW) and 4 Type 3 wind generators. Battery capacity varies widely (108–372 kWh), as does grid connection (0–292 kW). Despite varying initial investments, Scenarios II-VII show a consistent 4-year Internal Rate of Return, indicating good economic potential across different configurations.

Airline Price prediction using ExtraTrees Regressor

Airlines employ dynamic pricing strategies, which are based on demand estimation models, to set ticket prices. Airlines often set the price of their seats based on the necessary demand because they can only sell a certain number of seats on each journey. Airlines typically raise travel costs during times of strong demand, which slows down the rate at which seats are filled. When demand declines and seats become unsold, airlines typically lower their ticket prices to draw in new passengers. It will be more beneficial to sell those seats for more than the cost of service per passenger because unsold seats result in a loss of revenue. In order to forecast airline ticket prices, the primary objective of this study was to determine the factors influencing airline ticket costs and look into the relationships between them. Consequently, a model is created to predict the cost of plane tickets, allowing consumers to make better-informed decisions regarding their purchases. The cost of airline tickets in India is then predicted by this study using Four different machine learning algorithms: Extra Trees Regressor, XG Boost Regressor, Random Forest, and Decision Tree. Furthermore, hyperparameter optimization is done to obtain the most accurate and ideal prediction results. The Extrat Trees regressor yielded the greatest results, with the lowest RMSE of about 1807.59 and the highest accuracy of nearly 88%.

Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation App

Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative-filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multichannel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.

Optimizing zoning for ecological management in alpine region by combining ecosystem service supply and demand with ecosystem resilience

In order to enhance ecosystem stability and promote sustainable regional ecological, social, and economic development, it is crucial to explore the coupling relationship between ecosystem service supply and demand and the resilience of ecosystem, so as to propose scientific ecological management zones and strategies. Taking the vulnerable alpine ecosystem in Gannan Tibetan Autonomous Prefecture (Gannan Prefecture) as the study area, this paper comprehensively utilized multi-source data, grid analysis, ecosystem service supply and demand estimation model, and coupled coordination model to analyze the spatio-temporal differentiation and coordination pattern of ecosystem service supply and demand in the study area from 2000 to 2020. With the assistance of the Analytic Hierarchy Process (AHP), the ecosystem resilience index system was constructed to evaluate the regional ecological resilience. The results reveal the following: (1) In the past 20 years, the ecosystem service supply and resilience in Gannan Prefecture showed a fluctuating upward trend, and the demand continued to grow steadily. Their spatial differentiation were obvious, but the pattern remained stable. (2) There was a moderate incoordination indicated by the average coordination degree of the supply and demand coupling of ecosystem services, which rangeed between 0.3 and 0.4. (3) Gannan Prefecture was split into three ecological management zones, considering the spatial distribution of ecosystem service supply and demand, as well as resilience. Through system function monitoring and other measures, the ecological conservation zone will rely on its high resilience to support the restoration and self-sufficiency of the system, ensuring the stability and well-being of the ecosystem. The primary objectives of general protected zone includes environmental preservation, strict regulations, and the prevention of human intervention. To enhance their ecological background, key restoration zone must intensify the implementation of ecological restoration initiatives. To address the needs of the locals, strategies such as ecological compensation, optimizing the land use structure, and fostering the growth of environmentally friendly companies can be implemented simultaneously.

Review on Parking Demand Estimation Based on Crowdsourced Data

With the increase in urbanization and the growth of the economy, car ownership increases significantly, and at the same time demand for parking increases causing several issues like traffic congestion and resource wastage. There’s a critical significance of accurate parking demand estimation in the context of urban planning, addressing those issues. It emphasizes the emerging role of crowdsourced data as a novel solution for more efficient and cost-effective parking demand estimation. This review discusses the benefits of crowdsourcing, including real-time data and minimal infrastructure requirements, while acknowledging challenges like data accuracy, user privacy, and potential biases. Furthermore, it provides a comprehensive overview of the subject matter and it suggests future directions for improvement, proposing the integration of advanced technologies like Google APIs and IoT to augment parking demand estimation models and address the limitations associated with crowdsourced data.

The impact of brand equity on profit premium in an equilibrium framework

AbstractRecent research extends the estimation and analysis of structural demand and supply models to consider the concepts of brand equity and brand value and their role in product investment decisions. In this paper, we analyze data from the Dutch market for new cars to show that differences in brand equity may also entail significant differences in marginal costs. Next, we illustrate that ignoring the role of brand equity in marginal costs, as the existing literature has, ignores the possibility that investments in brand equity may actually reduce the marginal profits for the offering. This can change investment incentives and produce different market structures.

Research on optimization layout of hydrogen refueling facility network based on renewable energy hydrogen production mode

Within the transportation sector, hydrogen fuel cell vehicles represent the most efficient method of harnessing hydrogen energy. The establishment of a hydrogen refueling facility network that encompasses the entire cycle of hydrogen energy production, transportation, and application contributes to the effective promotion of hydrogen fuel cell vehicles. However, existing research predominantly focuses on hydrogen production and the conversion of refueling stations, neglecting the economic and stability considerations of the full-cycle use of hydrogen energy. This study proposes a hydrogen refueling facility network planning model that utilizes hydrogen energy throughout its full cycle. Firstly, a hydrogen demand estimation model based on the application scenarios of hydrogen fuel cell vehicles which clarifies the hydrogen requirements in each scenario is proposed. Regional hydrogen energy needs and the costs of building hydrogen refueling stations are then considered, using a set cover model to optimize the overall layout of existing and new refueling stations. Finally, taking into account the supply costs of renewable energy hydrogen production points and regional supply stability, a hydrogen refueling facility network planning model is constructed. The Beijing-Tianjin-Hebei region is selected as a case study, establishing a large-scale hydrogen supply network based on renewable energy hydrogen production, transportation, and usage.

A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing

Random Consideration Set Model We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176]—that captures the limited attention of consumers, assuming that purchases are based on fixed preference orderings with consideration sets formed from independent attentions. We provide a condition for uniquely identifying model parameters and design an efficient algorithm for model parameters estimation. We offer a greedy-like algorithm for assortment optimization, adaptable for optimal assortment subject to cardinality constraint or discovering efficient sets. We extend the model to consider random product preferences, with a 1/2 performance-guaranteed approximation algorithm. Using data from a major U.S. airline, we find that the RCS model outperforms the mixed multinomial logit model in approximately half of the markets, particularly with smaller, less varied data sets.

Housing affordability and housing demand assessment for urban poor in India using the hedonic model

Purpose This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households. Design/methodology/approach A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding. Findings Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R2 > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects. Research limitations/implications This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums. Practical implications All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas. Originality/value This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

Minimum inertial demand estimation of renewable energy considering new power system frequency constraints using sparrow search algorithm

With the proposal of a "dual carbon strategy " and the rising share of renewable energy sources, the resulting low-inertia problem has seriously affected the frequency stability of new power systems. This paper proposes a minimum inertial demand estimation of renewable energy considering new power system frequency constraints to enhance the power system inertia situational awareness and clarify the system operation boundary. Firstly, the frequency response model with multi-source renewable energy units and the time-domain mathematical expressions are established, and the frequency constraints and inertial demand of new power systems are given. The minimum inertial demand estimation model is constructed on this basis, which can be solved by the sparrow search algorithm (SSA). Ultimately, the applicability and practicality of the suggested approach are demonstrated and verified in IEEE 39-bus system. The case study results demonstrate the process can accurately calculate the inertial demand of renewable energy side considering system frequency constraints and give guidance for the stable operation of new power systems.

Research on path following control system of wave gliders based on maneuverability demand estimator

The wave glider (WG) with a propeller-rudder system represents a novel kind of unmanned surface vehicle that has a hybrid driving capability of wave energy and solar energy, compared to the WG with only a rudder system. The propeller-rudder system can improve the flexibility and maneuverability of the WG, especially in low-speed and nearshore operations. However, owing to the limited solar power generation and high energy consumption of the propeller, studying the precise control switching strategy of the propeller is of great significance. In this paper, a path following control system is proposed for the WG with a propeller-rudder system based on an adaptive line-of-sight algorithm (ALOS), a maneuverability demand estimator and a maneuverability controller. The maneuverability demand estimator is determined by the cross-track error and sideslip angle. And the cross-track error and sideslip angle are fused by using fuzzy mathematics to establish the mobility demand estimation model. The maneuverability demand index (MDI) intuitively expresses the urgency of the mobility demand during the path following process and is input to the maneuverability controller to control the propeller revolution. Through numerical simulations and sea trials, the effectiveness and superiority of the proposed path-following control system is verified.

Using consumer preference data in forecasting demand in apparel retailing

PurposeThis study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.MethodologyTo collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.FindingsThe respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.Research implicationsThere has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.Practical implicationThe proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.Originality/valueThis research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

Deep fuzzy mapping nonparametric model for real-time demand estimation in water distribution systems: A new perspective.

Hydraulic modeling has been recognized as a valuable tool for improving the design, operation, and management of water distribution systems (WDSs) as it allows engineers to simulate and analyze behaviors of WDSs in real time and help them make scientific decisions. The informatization of urban infrastructure has motivated the real-time fine-grained control of WDSs, making it one of the hotspots in recent years, thereby putting higher requirements on WDS online calibration in terms of efficiency and accuracy, especially when dealing with large-complex WDSs. To achieve this purpose, this paper proposes a novel approach (i.e., deep fuzzy mapping nonparametric model (DFM)) from a new perspective for developing a real-time WDS model. To our knowledge, this is the first work that considers uncertainties in modeling problems using fuzzy membership functions and establishes the precise inverse mapping from pressure/flow sensors to nodal water consumption for a given WDS based on the proposed DFM framework. Unlike most traditional calibration methods that require time to optimize model parameters, the DFM approach has a unique analytical solution derived through rigorous mathematical theory, thus the DFM is computationally fast as a result of sensibly handling the problems whose solutions typically require iterative numerical algorithms and large computational time. The proposed method is applied to two case studies and the results obtained show that it can produce a real-time estimation of nodal water consumption with higher accuracy, computational efficiency, and robustness relative to traditional calibration methods.

Reproducible air passenger demand estimation

The availability of passenger demand estimates for air traffic routes is crucial to a plethora of application and research problems ranging from, e.g., optimization of airline fleet utilization to complex simulations of whole air transport systems. However, somewhat surprisingly, such demand estimates appear hard to come by directly or even to generate by means of published models. This is in large parts due to the widespread use of expensive proprietary data (such as airline-specific ticket prices for certain flight connections) which is typically employed both to calibrate demand estimation models as well as to evaluate such models in order to obtain demand estimates for given origin–destination airport pairs. With this work, we propose building a data set for the European air transport system from given base data and automatically extracted and processed data from external sources, all of which are (made) freely available in the public domain and thus enable reproducibility and facilitate comparability of research involving air passenger transportation. Moreover, we challenge the long-standing tradition of calibrating so-called gravity models for demand estimation by standard linear regression. For the European air transport system, using the aforementioned publicly available data, we demonstrate that machine learning models and techniques like neural networks or the “kernel trick” can significantly improve the estimation quality with respect to ordinary least-squares. In fact, our results—the best of which were obtained using our feed-forward neural network model (four hidden layers with tanh and ReLU activations)—achieve a performance at least comparable to what has been reported in earlier works that utilized non-public data. Computer code to generate air passenger demand estimates is made publicly available online along with our base data and implementation to collect and curate external data.

Employment Intensity of Economic Growth in Southern Europe: Evidence from Multidimensional Panel Data

This paper examines the employment intensity of economic growth in Southern Europe during the so-called “post-crisis” recovery years. A labor demand estimation model based on multidimensional panel data from 2010 to 2019 was utilized. Findings from our macroeconomic analysis of eight different industries refute the predictions of neoclassical labor theory in the region. The results further indicate the presence of jobless growth in the areas of overall employment, fulltime employment, and overall employees. They also signal that economic growth may have created job opportunities in part-time and youth employment, and among temporary employees, rather than full-time jobs. The paper links these findings to particular characteristics of the regional labor market, discusses their implications for understanding unemployment and formulates recommendations for future policy.

Research on the balance optimization of investment demand and investment capability of power grid enterprises

In the context of the in-depth advancement of the power system reform and the increasingly stringent supervision of state-owned assets and state-owned enterprises, it is urgent to clarify the balance between investment demand and investment capacity of the power grid. First, the investment capacity and investment demand estimation models are established respectively, and then a dynamic balance optimization model between investment capacity and investment demand is established. By adjusting the range of adjustable indicators of the investment ability and demand calculation model, the objective is set to be the smallest difference between the investment ability and the investment need, the Quantum Genetic Algorithm is used to solve the problem. And consider future development scenarios to achieve a dynamic balance between investment ability and the investment need. Through the analysis of calculation examples, it is known that the investment capacity of a certain place is 630 million yuan, and the investment demand is 661 million yuan. Combined with the development of related factors affecting the investment in the future, when the relevant factors are at a certain value, the two reach a balance, and the investment scale after the balance is 649 million yuan, which verifies the feasibility of investment optimization of the investment scale, so as to support the investment decision of power grid.

지방자치단체의 사회복지관 설치를 위한 수요추정 방법과 적용에 관한 탐색적 연구

The purpose of this study is to prepare a reasonable and simple application methodology to help calculate the appropriate supply of social welfare centers in local governments. To this end, a demand estimation model was derived by reviewing previous research on facility supply and the minimum standards for basic living infrastructure supply by the Ministry of Land, Infrastructure and Transport.
 The social welfare center supply estimation model presented in this study was set with four main variables. First, the actual demand for social welfare centers was counted as potential targets, centering on the low-income and vulnerable class, and then set as the demand for facilities. Second, it was set as a capacity variable that can be used in one social welfare center, centering on the annual available population at the social welfare center. Third, the average moving distance between the administrative welfare center and the social welfare center was set. This is a variable considering accessibility. Fourth, the separation distance between social welfare centers was set to consider spatial equity.
 Based on these four variables, Seongnam City was applied as a case study, and the appropriate number of social welfare centers with a population of 920,000 was derived as 14. The strength of this study is that first, the capacity of social welfare centers was considered in the model. Second, local governments can use it simply.

Identification and Estimation of Demand Models with Endogenous Product Entry and Exit

Identification and Estimation of Demand Models with Endogenous Product Entry and Exit