Demand Model Research Articles

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

Online Learning and Pricing for Multiple Products With Reference Price Effects

ABSTRACTWe consider the dynamic pricing problem of a monopolist seller who sells a set of mutually substitutable products over a finite time horizon. Customer demand is sensitive to the price of each individual product and the reference price which is formed from a comparison among the prices of all products. To maximize the total expected profit, the seller needs to determine the selling price of each product and also select a reference product (to be displayed) that affects the consumer's reference price. However, the seller initially knows neither the demand function nor the optimal reference product, but can learn them from past observations on the fly. As such, the seller faces the classical trade‐off between exploration (learning the demand function and reference price) and exploitation (using what has been learned thus far to maximize revenue). We propose a rate‐optimal dynamic learning‐and‐pricing algorithm that integrates iterative least squares estimation and bandit control techniques in a seamless fashion. We show that the cumulative regret, that is, the expected revenue loss caused by not using the optimal policy over periods, is upper bounded by where hides any logarithmic factors. We also establish the regret lower bound (for any learning policies) to be . We then generalize our analysis to a more general demand model. Our algorithm performs consistently well numerically, outperforming an exploration‐exploitation benchmark. The use of price experimentation and estimation techniques could be readily applied in real retail management.

The impact of long-term care insurance on the health status and healthcare expenditure of older adults in China

ObjectiveLong-term care needs have grown with population aging. This study explores the relationships among health status, healthcare expenditure, and long-term care insurance (LTCI) among the older adults.MethodsUsing data from the China Health and Retirement Longitudinal Study (CHARLS 2011, 2013, 2015, 2018, and 2020) and based on the demand model for healthcare services, this study employed the difference-in-difference (DID) method to assess how the implementation of LTCI contributed to the health status and healthcare expenditures of the older adults. The propensity score matching DID method (PSM-DID) and other tests were used to conduct further robustness checks.ResultsThe findings demonstrate a significant positive impact of LTCI on improving the health status and reducing healthcare expenditures in the elderly population. The PSM-DID indicates that LTCI can effectively improve the health status of the elderly population and reduce their healthcare spending.ConclusionsBased on the findings, the development of relevant policy frameworks for LTCI in China is recommended. These include consider the differences among the various pilot cities and social strata to allow policy adjustments and improvements in a timely, establish a dynamic and diversified long-term care insurance financing mechanism, encourage collaboration between medical institutions and elder care facilities, establish effective contact between LTCI and medical institutions, and use incentive policies such as tax relief to provide financial support and subsidies.

Spatio-temporal forecasting model for EV charging demands

Spatio-temporal forecasting model for EV charging demands

Development of A Demand Model for School Trips in Colombo, Sri Lanka

Development of A Demand Model for School Trips in Colombo, Sri Lanka

Inference and Impact of Category Captaincy

This paper studies category captaincy, a vertical relationship where retailers delegate shelf placement and pricing decisions of an entire product category to one of the leading manufacturers within that category. These contracts involve preferential treatment given to certain brands within the retailer. However, they are confidential, and empirical analysis has been limited. To study the prevalence and welfare impact of these contracts, I develop a novel approach to infer the presence of captaincy contracts based on preferential treatment in shelf placement and pricing. I first classify retailers into different captaincy types based on a brand × retailer specific quality of shelf placement inferred from a demand model. I then conduct pricing tests and find that captains, in line with theoretical predictions and industry anecdotes, eliminate double markups from their own products but not from competitor products. Comparative statics show that captaincy relationships increase the market share of the captain, but they can also increase consumer welfare by about 5% due to the elimination of double markups on the captain’s products. This paper was accepted by Raphael Thomadsen, marketing. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.02039 .

Tax policy and tobacco consumption in Botswana: an ARDL-EC approach

PurposeBotswana imposed a 30% ad valorem tobacco tax in 2014 to reduce tobacco use. The purpose of the paper is to assess the effectiveness of the tax in curtailing tobacco consumption.Design/methodology/approachAn autoregressive distributed lag and equilibrium correction (ARDL-EC) framework is applied on data for the period 1975–2020 to estimate a dynamic tobacco demand model. The estimated price elasticity is then used to quantify the effects of the tax on tobacco consumption.FindingsA 10% rise in the tobacco price results in a 6.6% decrease in tobacco consumption, suggesting an inelastic response. A 10% rise in income yields a 12% increase in tobacco consumption, reflecting that the rapid economic growth Botswana experienced post-independence yielded increased tobacco use. Tobacco consumption declined by 3.6% per year, possibly capturing the effects of increasing awareness of the adverse effects of tobacco use over time. The 30% tobacco tax yielded a 20% reduction in tobacco consumption, suggesting moderate effectiveness in curtailing consumption. The tax reduced annual tobacco consumption by 100 grams per capita or 151 metric tons nationally.Research limitations/implicationsFuture research could explore the effects of non-price anti-tobacco measures and socioeconomic and demographic factors on tobacco use to provide further insights for guiding the development of targeted anti-smoking interventions.Originality/valueTobacco demand elasticities vary across countries and analytical methods. Therefore, country-specific empirical evidence is essential for policymaking. An existing study in Botswana employed cross-sectional analysis, which does not capture the addictive effects of tobacco. The ARDL-EC framework is employed to close this gap. Simulated effects of the tax are useful for policy reform in Botswana.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2024-0097

Technical Note—Revenue Management with Calendar-Aware and Dependent Demands: Asymptotically Tight Fluid Approximations

Revenue Management with Dependent Demands In the revenue management literature, a standard approach to model the demand is based on dividing the selling horizon into a number of small time periods such that there is at most one customer arrival at each time period and that the customer arrivals at different time periods are independent of each other. Under this demand model, if the mean demand for a product increases with rate T, then the standard deviation of the demand must increase with rate square root of T. In other words, large demand volume and large demand variability cannot coexist. In “Technical Note—Revenue Management with Calendar-Aware and Dependent Demands: Asymptotically Tight Fluid Approximations,” Li, Rusmevichientong, and Topaloglu consider demand models that can simultaneously incorporate large demand volume and large demand variability while also allowing dependence between the demands over nonoverlapping time intervals. Under such demand models, they give efficiently computable policies that admit performance guarantees.

The application of predictive stochastic model based on Monte Carlo method in building energy saving renovation

Abstract: This research has established an energy consumption prediction model based on the Monte Carlo method to resolve the energy-saving transformation problem. First, simplify the building to construct the proposed model. Second, through the principle of building energy balance and Monte Carlo method, the cooling and heat demand model of regional buildings and the energy consumption prediction model of regional buildings are built. Finally, the energy consumption simulation and energy consumption prediction of the regional building complex after energy-saving renovation are carried out. The experiment shows that the building energy consumption in July and August was relatively high, reaching 2.36E+14 and 2.4E+14, respectively. The energy consumption in April and November was relatively low, reaching 1.2E+14 and 1.4E+14, respectively. The highest prediction error was in November, reaching 12%. The lowest prediction error was in January and February, only about 2%. The error of monthly energy consumption predicted by Monte Carlo method is less than 12%, the Root-mean-square deviation is 5%, and the error between predicted and actual annual total energy consumption is only about 2%. By comparing the predicted energy consumption after energy-saving renovation with before, the energy-saving rate reached about 20%. The research results indicate that the proposed Monte Carlo based predictive stochastic model exhibits good predictive performance in building energy-saving renovation, providing theoretical guidance and reference for feasibility studies, planning, prediction, decision-making, and optimization of building energy-saving renovation.

AI-Driven Optimization of Last-Mile Delivery

The optimization of last-mile delivery represents a critical challenge in modern e-commerce logistics, consuming a substantial portion of total shipping costs. This comprehensive technical article examines how artificial intelligence and machine learning technologies are revolutionizing last-mile delivery operations through advanced route optimization, demand forecasting, and resource allocation. This article synthesizes findings from recent implementations across major logistics providers, demonstrating that AI-driven route optimization systems significantly reduce delivery times and decrease fuel consumption across diverse operational environments. Analysis of machine learning models deployed by leading e-commerce platforms shows marked improvements in delivery time prediction accuracy, achieving unprecedented precision in estimated delivery windows. This article examines key technical components, including dynamic routing algorithms, predictive demand modeling, and real-time fleet management systems, while also addressing implementation challenges such as data quality and system integration. Case studies from major logistics providers demonstrate substantial ROI improvements following AI implementation, with particular emphasis on neural network architectures optimized for urban delivery scenarios. This article provides a technical framework for understanding how AI technologies transform last-mile logistics while offering insights into future developments, including autonomous delivery systems and smart city integration.

Longitudinal Monitoring of Electric Vehicle Travel Trends Using Connected Vehicle Data

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users.

A Review of Activity-based Disaggregate Travel Demand Models

This paper reviews the literature on disaggregated travel demand models from a choice perspective, focusing on activity-based models (ABMs) and synthetic population generators as example approaches to capture these choice dimensions. We discuss the links between these models and elaborate on their integration, emphasizing how advanced population synthesis can address gaps in ABMs. The importance of incorporating multi-person and multi-period contexts to more accurately represent travel behavior is highlighted. Further advances aimed at improving the practical application of these models are also discussed.

An interlinked dynamic model of timber and carbon stocks in Japan's wooden houses and plantation forests

Carbon absorption in growing trees is an important element of a carbon-neutral society, and the long-term storage of carbon stocks is a crucial sustainability challenge. Previous studies have focused on either live-biomass carbon stocks in plantation forests or anthropogenic carbon stocks in man-made objects. For a comprehensive nature-based climate solution, an analytical framework, dataset, and scenario setup for modeling the interrelationship between timber supply and demand are required. This study developed an interlinked material flow analysis model in which the timber demand for wooden houses is connected with timber supply from managed plantation forestry. We demonstrate the model by quantifying both live-biomass and anthropogenic carbon stocks and their potentials in Japan. We compared multiple scenario-runs of the model for wooden house demands estimated by population change with varying combinations of house types, structures, and lifespans. Our results show that carbon stocks will reach a maximum amount of 1.1 billion t-C by 2050 in a scenario of high demand for wooden detached houses with lifespan extensions. On the other hand, we also found that the aging of plantation forests and their reduced carbon-stocking capacities appear inevitable in any scenario owing to the limited demand for timber. Notably, despite the widely different settings of the various scenarios, our results exhibited narrow variances in future potential carbon storage in Japan. This can be explained by the unique population characteristics and building demographics of Japan. These counterintuitive findings highlight the need for interrelated modeling of the forestry and construction sectors. Our model and its scope are versatile and applicable to other case study areas, estimation periods, and target materials.

Opportunities and roadblocks in the decarbonization of the global steel sector: a demand and production modelling approach

Opportunities and roadblocks in the decarbonization of the global steel sector: a demand and production modelling approach

Air pollution reduction co-benefits associated with low-carbon transport initiatives for carbon neutrality in China by 2060

Low-carbon transport policy measures will contribute to greenhouse gas emission reductions, as well as improvements in air quality via air pollutant emission reductions. Here, we developed a regional transport energy model by integrating a transport demand model and an energy system model to explore the associated air pollutant emission reductions achieved by low-carbon transport initiatives when projecting a path towards carbon neutrality. Several scenarios were created to identify the effectiveness and feasibility of low-carbon strategies using the Avoid–Shift–Improve (A-S-I) framework including “Technology”, “Regulation”, “Information”, and “Economy” instruments. Significant reductions in air pollutant emissions alongside transport decarbonization in China could be realized by implementing policy measures to establish effective transport demand management, a modal shift in transport use, and technological improvements. In particular, the “Improve” strategy was more effective in delivering significant reductions in air pollutant emissions than were the “Avoid” and “Shift” strategies, but the policy effects of each A-S-I pillar varied greatly for different pollutants and across different regions. An air pollution reduction strategy for the transport sector should be designed in an integrated manner based on a series of different strategies and tools.

Developing Causal Loop Diagram for Urban Development and Land Carrying Capacity in Surakarta

Surakarta has an area of 46.72 km2 with a population of 583,961 people. The land use in Surakarta for housing is 62% and for open space is only 9.82% of the area. Meanwhile, economic growth in this city has always experienced an increasing trend, where in 2023 investment growth reached 55.82%. This study aims to determine the general picture through the causal relationship between the components of urban development and the carrying capacity of land in Surakarta. This study uses a quantitative descriptive approach. Data is obtained from identified variables which are then arranged dynamically using Causal Loop Diagram (CLD) analysis processed with Vensim software. The results show that there is a supply model (built-up area and green open space) and a demand model (population, investment, and Gross Domestic Product). The relationship between variables describes the causal relationship in Surakarta's Land Carrying Capacity divided into three rounds, namely Loop B1 (Land Carrying Capacity – Demand - Supply - Land Carrying Capacity), Loop B2 (Supply - Built-up Land - Land Conversion - Green Open Space – Supply), and Loop R1 (Population - Demand - Investment - GDP - GDP per capita – Population).

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

A study on the combination of crystallization-controllable phase change materials and solar-assisted heat pump for electricity demand shifting in space heating

Supercooled phase change materials offer a promising solution for space heating due to their ability to release latent heat upon crystallization initiation, even when stored at ambient temperatures. This unique property makes them ideal for solar-assisted space heating, where external activation enables on-demand heat release, addressing the critical need for energy-efficient heating solutions. In this study, a system promoting demand shifting is proposed, aiming to transfer energy consumption from morning and evening peak periods to daytime and high solar irradiance days, thereby enhancing the efficiency of solar heat pumps and reducing grid stress through the use of supercooled crystallization-controllable phase change materials. A model was developed, consisting of evacuated tube collectors, a buffer tank, heat storage tanks with crystallization-controllable phase change material, and a building heating demand model. The study introduces a novel system control methodology, focusing on an effective operation of tank shifting based on the heating requirement and solar energy availability. Real weather data were used to calculate system performance. With 50 m2 of collectors, a 1000-liter buffer tank, and a heat pump with a maximum output of 7 kW, the heat storage tanks are charged and discharged following the developed operational methodology. The system achieved a weekly coefficient of performance of 3.56 and successfully shifted electricity demand to solar hours, with only 28.5% of the total consumption occurring during domestic morning and evening peak times.

Seismic resilience of deteriorating bridges under changing climatic conditions

This study underscores the critical need to integrate changing climatic conditions into corrosion models for civil engineering infrastructures, particularly highway bridges, given the potential reduction in structural performance post-seismic events. The paper introduces a novel framework for assessing the seismic resilience of deteriorated highway bridges in the context of changing climatic conditions. The framework is demonstrated on a non-seismically designed simply supported highway bridge situated near the sea in a seismically active region of Gujarat, India. An improved corrosion deterioration model is used that considers the impact of climate change and non-uniform pitting corrosion for evaluating the deterioration of RC bridge components. A detailed three-dimensional finite-element model of the case-study bridge is developed that can accurately simulate various failure modes of corroding bridge piers. Time-varying seismic fragility curves are developed using damage limit states and probabilistic seismic demand models while considering the influence of climate change. Bridge seismic resilience is estimated by aggregating the seismic vulnerability, losses, and recovery functions. Results show that incorporation of changing climatic factors will considerably reduce the seismic resilience of the 75-year corroded bridge up to 56 %. Finally, a comparison of seismic fragility and resilience is carried out using the proposed and conventional corrosion deterioration model to evaluate the significance of considering the effects of climate change in the seismic resilience assessment framework.

Exact algorithms for continuous pricing with advanced discrete choice demand models

Abstract We present a spatial Branch and Bound and spatial Branch and Benders Decomposition approach together with the Breakpoint Exact Algorithm (BEA) to tackle the uncapacitated choice-based pricing problem (CPP) where demand is captured by a discrete choice model (DCM) based on the random utility principle. We leverage problem characteristics to reformulate the state-of-the-art simulation-based formulation of the CPP as a mixed-integer linear program (MILP) into a non-convex quadratically constrained quadratic program (QCQP), and then into a non-convex QCQP with linear objective (QCQP-L). We solve this reformulation with an efficient spatial Branch and Bound procedure utilizing the McCormick envelope for relaxations, which are then solved using Benders decomposition. We further exploit utility breakpoints to develop the BEA, which scales polynomially in the number of customers and draws, providing a fast option for low numbers of prices. Our methods are evaluated against solving the MILP, QCQP, or QCQP-L with GUROBI on a mixed logit (ML) parking space operator case study. We outspeed the MILP by several orders of magnitude when optimizing one or two prices and reduce computational time drastically for larger numbers of prices. When comparing to algorithms tailored for the CPP with ML demand specifically, our approaches significantly outperform the state of the art. Our methodology suits all choice-based optimization problems with linear-in-price utilities, given any DCM.