Choice Model Research Articles

From policy to practice: The role of national policy instruments and social barriers in UK energy efficiency adoption in households

This paper investigates the adoption trends for energy efficiency (EE) measures in the UK residential sector and the extent to which national-level EE policies impact households' willingness to adopt EE measures and technologies. Using survey data from a representative sample of more than 2000 households across the UK and discrete choice models, this research examines the preferences given to different policy schemes such as tax rebates, grants, loans, easing administrative barriers or information campaigns; and its role on household decisions to adopt EE measures. We find that national-level policies in the form of grants and subsidies, information campaigns on savings and a reduction of administrative barriers would positively impact the probability of a household to adopt such measures. Differences are found between the policy drivers in the adoption of low-cost EE measures e.g. loft insulation vs. High-cost EE measures e.g. heat pumps. Importantly, our results highlight the role of policy mixes in fostering the adoption of EE measures in UK. In combination with other policies, tax credits can be effective in fostering the adoption on EE measures. Our findings have implications for policymakers seeking to enhance the effectiveness of EE policies and address social challenges to promote sustainable energy use.

Cycling or Ropeway – Two Choices of Environmentally Friendly Urban Modes

Ropeways have proven to be a suitable extension to public transport in Latin American cities. Numerous ropeway projects are being discussed in Europe. These have not yet progressed beyond the planning stage because, among other reasons, no assessments of potential demand have been carried out. Current travel demand models do not consider mode-specific properties of ropeways and cycling competing with public transport. Based on a stated choice experiment, a mode choice model for people without car availability was estimated. Besides standard attributes like travel times and costs, the impact of mode-specific attributes like service rates, crowding, reliability, personal attitudes and weather conditions were tested. A Mixed-Logit model was estimated showing significant influence of these attributes on mode choice in Graz, an Austrian city with a mode share of 20% each for cyclists and public transport. The introduction of a ropeway can increase the share of public transport, as the high capacity and reliability of ropeways have a positive effect. However, it is unlikely that some groups of cyclists will use the ropeway, as travel time and cost are factors that are more important. The database and the modeling approaches can also be applied to other ropeway projects.

Has the COVID-19 pandemic affected travellers’ willingness to wait with real-time crowding information?

Travel preferences in public transport (PT) have been substantially affected by the COVID-19 crisis, with rising emphasis on on-board safety and comfort aspects. Hence, real-time crowding information (RTCI) might have become even more instrumental in supporting travel decisions in congested urban PT systems. This study investigates the willingness to wait (WTW) to reduce (or avoid) overcrowding with RTCI in urban PT (bus and tram) journeys, analysing pre- vs. post-COVID travel behaviour attitudes. Stated-preference data and (subsequently estimated) choice models indicate that, while the pre-COVID WTW was primarily driven by mere possibility to avoid an overcrowded first departure, the post-COVID propensity to wait is strongly associated with expectations of seat availability in second departure as well. The ex-post WTW with RTCI seems to have become less-dependent on individual characteristics and more prominent for time-critical (obligatory) trips as well. Our findings underpin the rising relevance of passenger overcrowding in urban PT journeys. Moreover, they help better understand the potential of RTCI in post-pandemic recovery of PT ridership.

Movement decisions driving metapopulation connectivity respond to social resources in a long-lived ungulate, bighorn sheep (Ovis canadensis).

The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource-access to receptive mates-produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance 'foray' movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Network Analysis of Two-Stage Customer Decisions with Preference-Guided Market Segmentation

Abstract Network-based analyses have shown their effectiveness in understanding customer preferences through interactions and relationships between customers and products, particularly for tailored product design. There is limited research on applying this analysis to diverse customers with varied preferences. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers' two-stage decision-making process: consideration-then-choice. In heterogeneous markets, like household appliances, even customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, In this method, we segment customers into groups based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using Joint Correspondence Analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer-product networks and apply the Exponential Random Graph Model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how a deeper understanding of customer preferences at different decision stages can inform and refine design strategies, ensuring products are more closely aligned with the dynamic needs and tastes of diverse markets.

Mouse Cardiovascular Imaging.

The mouse is the mammalian model of choice for investigating cardiovascular biology, given our ability to manipulate it by genetic, pharmacologic, mechanical, and environmental means. Imaging is an important approach to phenotyping both function and structure of cardiac and vascular components. This review details commonly used imaging approaches, with a focus on echocardiography and magnetic resonance imaging, with brief overviews of other imaging modalities. In this update, we also emphasize the importance of rigor and reproducibility in imaging approaches, experimental design, and documentation. Finally, we briefly outline emerging imaging approaches but caution that reliability and validity data may be lacking. © 2024 Wiley Periodicals LLC.

Weak identification in discrete choice models

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Lastly, we apply our approach in two empirical examples: married women labor force participation, and US food aid and civil conflicts.

Evaluating the influence of cyclists’ route choices incorporation into travel demand modelling: A case study in greater Helsinki

Cycling is a sustainable transport mode that endorses an active lifestyle. While cycling shows great potential, it is essential for urban planning to consider attributes influencing the choices that cyclists act upon. Cyclists’ route choices have been studied since the Eighties with knowledge being applied in cycling network planning. Yet, the role of cycling as a sustainable transportation mode has been largely absent from travel demand modelling. This paper researches cyclists’ route choice preferences and evaluates the opportunity of incorporating route choice modelling into travel demand modelling to improve the accuracy of cycling route choice. To this end, a route choice framework is developed in which a stated preference survey for data collection is conducted, a multinomial Logit model is applied to the data to identify the factors that significantly influence cyclists’ route choice behaviour. The generated route choice utility models are further integrated into an existing regional travel demand model to evaluate the performance of cyclists’ route choice modelling in the presence of additional factors. Then, the route choice model outputs are validated against two sets of external data. The results show that bike facilities, traffic volume, and trip length are the key factors influencing cyclists’ route choice preferences, and the generated route choice models can be an applicable improvement in incorporating the influences of cyclists’ preferences into travel demand modelling.

Impact of a pilot mHealth intervention on treatment outcomes of TB patients seeking care in the private sector using Propensity Scores Matching-Evidence collated from New Delhi, India.

Mobile health applications called Digital Adherence Technologies (DATs), are increasingly used for improving treatment adherence among Tuberculosis patients to attain cure, and/or other chronic diseases requiring long-term and complex medication regimens. These DATs are found to be useful in resource-limited settings because of their cost efficiency in reaching out to vulnerable groups (providing pill and clinic visit reminders, relevant health information, and motivational messages) or those staying in remote or rural areas. Despite their growing ubiquity, there is very limited evidence on how DATs improve healthcare outcomes. We analyzed the uptake of DATs in an urban setting (DS-DOST, powered by Connect for LifeTM, Johnson & Johnson) among different patient groups accessing TB services in New Delhi, India, and subsequently assessed its impact in improving patient engagement and treatment outcomes. This study aims to understand the uptake patterns of a digital adherence technology and its impact in improving follow-ups and treatment outcomes among TB patients. Propensity choice modelling was used to create balanced treated and untreated patient datasets, before applying simple ordinary least square and logistic regression methods to estimate the causal impact of the intervention on the number of follow-ups made with the patient and treatment outcomes. After controlling for potential confounders, it was found that patients who installed and utilized DS-DOST application received an average of 6.4 (95% C.I. [5.32 to 7.557]) additional follow-ups, relative to those who did not utilize the application. This translates to a 58% increase. They also had a 245% higher likelihood of treatment success (Odds ratio: 3.458; 95% C.I. [1.709 to 6.996]).

Risk premium, price of risk and expected volatility in the oil market: Evidence from survey data

This paper contributes to the literature on crude oil risk premiums by providing ex-ante measures of these premiums using survey oil price expectations over an extended period. These ex-ante premiums are uncorrelated with ex-post premiums commonly used in existing studies, whereas they are more relevant as they directly influence investors' decision-making. Utilizing a portfolio choice model, we explain the ex-ante premium as the product of the price of risk and the expected variance, both varying over time and across horizons. We estimate this relationship using a multivariate state-space framework. From our estimated risk prices we find, on average, that investors exhibit risk-seeking behaviour in the short term and risk aversion in the long term. It follows that the term structure of oil risk premiums are prominently upward-sloping. Additionally, consistent with the prospect theory, investors are found to be predominantly risk averse in a context of expected gains and risk-seeking in a context of expected losses. Finally, the dynamics of risk prices are shown to be driven by identifiable economic, financial, and oil market-related factors.

Gender Issues, Social Equity, and the Sustainable Management of Urban Transport Networks: An Italian Case Study

The paper contains transport and environmental analyses for the Gallipoli area and presents results from a consultation process involving transport operators, citizens, tourists, public employees, and stakeholders. Through statistical analysis of the data, the paper provides insights into the needs of specific user categories, highlighting gender issues and social equity as key aspects of urban policies. This paper further investigates users’ potential willingness to pay for general improvements in bus service quality and environmental conditions using discrete choice modeling. This study aims to address the challenge of understanding urban travel patterns and the underlying forces influencing user attitudes, which are often found in city surveys. Specifically, a Random Utility Model was used to profile users based on specially collected survey data. The results indicate the potential to meet latent systemic transport demand with more sustainable collective transport modes, particularly among women aged 19-29 who own a car. Simultaneously, there is a willingness to pay higher public transport tariffs for improved service, which is also largely environmentally sustainable. The paper can serve as a valuable resource for professionals in the transport and environmental sectors as well as for policymakers.

Time-varying relative risk aversion: Theoretical mechanism and empirical evidence

This paper explores the issue of understanding time-varying relative risk aversion with household-level data on two classical portfolio choice problems. First, we derive an analytic form solution to a parsimonious portfolio choice model with the preference given by Greenwood, Hercowitz and Huffman (1988, GHH), and then, the solution identifies four partial equilibrium effects in our model with the GHH preference on risky shares through two channels and two net effects whose signs hinge on the value of a key structural parameter. Based on household-level data, our empirical results from both mean and quantile regression models show clearly that wealth negatively affects risky shares and the estimated effects are statistically significant and robust, which is in line with the theory. Finally, we show that the GHH preference alone is not sufficient in explaining how risky shares respond to labor income in the household-level data.

Heatmap Design for Probabilistic Driver Repositioning in Crowdsourced Delivery

This paper proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning. Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .

Impact assessment of future fleet compositions in vehicle emissions in urban areas: A methodological framework and a case study

This study explores the impact of emerging vehicle technologies on direct urban traffic emissions. It investigates emission reduction potential from shifts in fleet compositions and modal choices, especially considering climate change. To achieve this, three key research areas are explored for historical, current, and future scenarios (up to 2050): mode choice, emission factors for different vehicle categories, and diverse vehicle propulsion technologies. The estimation of the modal split is pivotal, developing a methodology utilizing Stated Preference survey data, discrete choice modeling, Monte Carlo simulations, and macroscopic traffic simulations. Future scenarios derive from the reference year’s modal split, and emission factors and fleet compositions are predetermined via an extensive literature review, aiding the assessment of their respective emissions. A subsequent sensitivity analysis identifies the impact of specific parameters on emissions, guiding future research focus. Study results underscore differences in greenhouse gas emissions and primary air pollutants between base and future scenarios.

Interkingdom biofilm of Streptococcus pyogenes and Candida albicans: establishment of an in vitro model and dose-response validation of antimicrobials

Although Streptococcus pyogenes and Candida albicans may colonize tonsillar tissues, the interaction between them in mixed biofilms has been poorly explored. This study established an interkingdom biofilm model of S. pyogenes and C. albicans and verified the dose-response validation of antimicrobials. Biofilms were formed on microplates, in the presence or absence of a conditioning layer of human saliva, using Brain Heart Infusion (BHI) broth or artificial saliva (AS) as a culture medium, and with variations in the microorganism inoculation sequence. Biofilms grown in AS showed higher mass than those grown in BHI broth, and an opposite trend was observed for metabolism. The number of S. pyogenes colonies was lower in AS. Amoxicillin and nystatin showed dose-dependent effects. The inoculation of the two species at the same time, without prior exposure to saliva, and using BHI broth would be the model of choice for future studies assessing the effects of antimicrobials on dual S. pyogenes/C. albicans biofilms.

Logistics Model for Indonesia’s National Freight Model System: From a Deterministic to a Stochastic Framework

One of the components of a new model, called INTRAMOD, for Indonesia’s domestic freight transport is the logistics model. The logistics model describes shipment size choice and the choice between five different transport chain alternatives involving four main modes: truck, train, vessel, and plane. This paper presents the work to forecast the disaggregate transport chain and shipment size choices for Indonesia’s domestic shipments by applying a deterministic and a stochastic approach. Using a standard economic order quantity model with a consolidation assumption, a deterministic approach is used to determine the transport chain and shipment size, minimizing total logistics cost. As an alternative for this, a stochastic model aims to improve the logistics choice modeling by employing data on the manufacturer’s revealed preferences and stated preferences about only the transport chain choice. The chosen specification for the stochastic approach is utilizing the multinomial logit model. Using the demand elasticities for all alternatives with respect to changes in its transport cost, a comparison will be made between the two approaches. In addition, it is concluded that the deterministic model is susceptible to sticky and flip-flop behaviors. In contrast, this characteristic is absent from the stochastic approach.

Public transport mode choice behavior of different-income passengers during large-scale public health incidents

Public transport ridership has been hit hard by the COVID-19 pandemic in many countries. Investigating passengers' public transport mode choice behavior during large-scale public health incidents can uncover the major influential factors and help propose policies and strategies to reduce the pandemic transmission and recover the public transport revenue. This study develops an integrated choice and latent variables (ICLV) model by income based on structural equation model to model passengers' public transport choice behavior during the normalized stage of the pandemic. The model considers passengers' socioeconomic attributes, travel attributes, and attitude-perception attributes, and can appropriately capture passengers' psychological latent attributes. Taking Beijing China as an example, we collect some revealed preference survey data online. The modeling results show that the risk perception as a mediator variable has a significant impact on mode preference. Moreover, the convenience of public transport has the largest influence on risk perception. These findings suggest that risk perception and the convenience of public transport play a major role in passengers' mode choice behavior. In addition, the impacts of the various influential factors on the public transport mode choices are significantly different across different income groups. Further, the ICLV model can achieve better performance and is superior to the traditional Multinomial Logit model. The modeling framework can help propose targeted and instructive strategies during the normalized stage of the pandemic by uncovering the major influential factors in passengers’ public transport mode choices, which is applicable to similar pandemics in the future.

Understanding the influence of business innovation context on intentions of enrolment in master education of STEM students: a multi-level choice model

This article studies educational decisions, focusing on intentions of enrolment in master’s education of STEM bachelor students. Integrating human capital theory with concepts of cultural and social capital, we propose a two-level model for the choice of pursuing master’s degrees. First level (individual) includes factors covering individual habitus and organisational habitus (higher education institutions of bachelor students), while the second level (local) reflects the local business innovation environment. The proposed model was empirically tested on data collected from a sample of STEM and non-STEM bachelor students enroled in 10 public universities located in Romania. The results show that STEM students display a higher propensity to enrol in master's education, and the gap between STEM and non-STEM majors varies across regions. We find that educational decisions related to master’s degrees are shaped by local circumstances reflecting the business innovation intensity as more innovative business contexts are less conducive for enrolment of students in master programmes. In addition, the findings of the study show that local circumstances are not independent of the field of study when shaping students’ educational choices, highlighting the complex way in which individual and local levels factors interplay and shape educational decisions. STEM students’ propensity to enrol in master’s degrees is more influenced by the innovative business environment than other students. This study has implications for higher education policy and practice aiming to support longer educational careers in STEM.

Steering them softly with a quality label? A case study analysis of a patient channelling strategy without financial incentives.

Steering patients to lower priced and/or higher quality providers can increase the value of a healthcare system. In a managed care setting, health insurers may use financial incentives for this purpose. However, introducing cost-sharing differences among providers may cause enrolee discontent, which may result in disenrollment. Simply informing and guiding enrolees to preferred providers without financial incentives may therefore be an attractive alternative for insurers. But the effectiveness of such a soft channelling strategy is unclear. This paper investigates whether a Dutch health insurer's strategy of designating preferred hospitals for breast cancer surgery and for inguinal hernia repair affected its enrolees' hospital choices. In October 2008, preferred hospitals received a quality label ('TopCare') because of their high-quality performances in previous years. The insurer recommended these hospitals to enrolees without a financial incentive. Individual patient-level claims data from the insurer over a 5-year period (2006-2010) and a conditional logit choice model was used. Our study samples for breast cancer surgery and inguinal hernia repair included 7985 and 17,292 patients, respectively. It is found that for both procedures, patients ex ante already had a certain preference for the hospitals designated by the insurer as top-quality providers, even when considering possible additional travel time. Also, for both procedures, patient choice did not differ significantly before and after the launch of the TopCare label. The quality label did not increase patient demand for preferred hospitals. Thus, the insurer's strategy to steer patients to preferred hospital alternatives without a financial incentive was not effective.

Behavioral insights on individual and social adaptation to automated mobility using a serious game

IntroductionSerious games generate a virtual environment where players are immersed in simulated conflicts, guided by distinct rules. They replicate intricate systems, like transportation, encouraging social engagement via reinforced learning. Unlike preference-based studies, these games offer enhanced real-time feedback on players' actions. Thus, they reveal how user experience and social interaction influence decision-making over time. We use a serious game to study the willingness of travelers to adopt automated mobility, specifically shared modes of transport, an important step toward alleviating congestion, enhancing the quality of urban living, and improving people's health and well-being. MethodsFor each scenario, 100 participants were randomly divided into ten groups of ten interacting players. They chose independently out of three automated transportation modes - shared ride, shared car, and automated transit-over 50 simulated days how to commute to work. They aimed to maximize their overall score by arriving punctually, which was influenced by their mode and departure time and the choices of fellow players. Cross-nested logit kernel choice models were estimated based on the game data. ResultsIn the recurring congestion scenario players learned to adopt the shared ride at the expense of transit; in the nonrecurring congestion scenario, random incidents increased the use of transit and shared car (ride alone). ConclusionsCongested traffic motivated a shift to ridesharing at the expense of private rides and public transport; however, the latter was highly demanded when traffic became unsmooth and travel times more uncertain. The implications can be translated to health promoting polices to encourage sustainable travel behaviors while also improving transportation efficiency.