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Chandra R Bhat - One of the best experts on this subject based on the ideXlab platform.

  • Consumer Choice Modeling: The promises and the cautions
    Mapping the Travel Behavior Genome, 2020
    Co-Authors: Chandra R Bhat
    Abstract:

    Abstract It could be legitimately claimed that we are at a very fertile period in consumer Choice Modeling, with several important and exciting developments within the past decade in the data sources, theory, specification, estimation, and application of consumer Choice models. These developments have taken place in many disciplines, including marketing, transportation, regional science, psychology, economics, statistics, political science, and sociology. In this chapter, the author will discuss the multi-disciplinary evolution of consumer Choice models from being based on single source data to being based on multiple source data, from single discrete Choice to multiple discrete-continuous Choice, from single endogenous variable type to multiple endogenous variable types, from traditional likelihood estimation to the use of other estimation methods, and from simulation methods for estimation to also considering analytic approximation methods for estimation. At the same time, it is critical that we do not lose sight of the need for consumer Choice Modeling to continue to be grounded on fundamental behavior theories, rather than being viewed as a “blind” data-driven exercise, especially as we move into a new landscape of data ubiquity. The issue of “causality” versus “association” becomes central in this discussion.

  • introducing non normality of latent psychological constructs in Choice Modeling with an application to bicyclist route Choice
    International Choice Modelling Conference 2015, 2015
    Co-Authors: Chandra R Bhat, Subodh K Dubey, Kai Nagel
    Abstract:

    Economic Choice Modeling has continually seen improvements and refinements in specification, partly because of the availability of new techniques to estimate models. One such development is the incorporation of random taste heterogeneity ( i.e. , taste variations in response to explanatory variables) across decision makers using discrete (non-parametric) or continuous (parametric) or mixture (combination of discrete and continuous) random distributions for model coefficients. Such a specification also leads to correlations across alternative utilities when one or more random coefficients appear in the utility specifications of multiple alternatives. A second development is the explicit consideration of latent psychological constructs (such as attitudes, perceptions, values and beliefs) within the context of economic Choice models, which has the advantage (over the random taste heterogeneity approach) that it imparts more structure to the underlying Choice process based on theoretical concepts and notions drawn from the psychology field. Additionally, it provides the opportunity to efficiently introduce random taste variations and the concomitant correlations across alternative utilities. This second development, commonly referred to as integrated Choice and latent variable (ICLV) models, may be viewed as a variation of the traditional structural equation methods (SEMs) to accommodate an unordered-response outcome. Another area of intense research in the recent past, but originating more from the statistical field, is the consideration of non-normal distributions in Modeling data.  This has been spurred by the increasing presence of multi-dimensional data that potentially exhibit non-normal features such as asymmetry, heavy tails, and even multimodality. Parametric approaches to accommodate non-normality span the gamut from finite mixtures of normal distributions to skew-normal distributions (and more general skew-elliptical distributions) to mixtures of skew-normal distributions (and mixtures of more general skew-elliptical distributions). Many recent studies use either a multivariate skew-normal or a skew-t distribution as the basis for accommodating non-normality, with different proposals on how to characterize these skew distributions (see Lee and McLachlan (2013) for a recent review and synthesis of the many different proposals). However, it is well recognized now that the underlying basis for all of the different proposals for the multivariate skew-normal distribution originates in the pioneering work of Azzalini and Dalla Valle (1996). In the current paper, we bring together the two developments discussed above - the ICLV model structure and the treatment of non-normality through a multivariate skew-normal or MSN distribution specification. In particular, we allow the latent constructs in the ICLV model to be skew-normal. After all, there is no theoretical basis for specifying these constructs as normal (as is typically assumed in the literature); thus, there is substantial appeal in specifying a more general non-normal specification that is then characterized empirically.  To our knowledge, this is the first such ICLV model proposed in the econometric literature, which has several important features. First , it recognizes the very real possibility that latent variables are non-normally distributed after conditioning on exogenous variables. Incorrectly imposing normality will, in general, lead to econometrically inconsistent and inefficient estimation in all of the ICLV model components. Second , our proposal to include non-normality exploits the latent factor structure of the ICLV model. That is, our approach constitutes a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Third , taste variations ( i.e. , heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. Thus, for example, in a bicyclist route Choice model, bicyclists who are more safety conscious (say a latent variable) than their peers may be more sensitive to motorized traffic volumes and on-street parking. By interacting safety consciousness with exogenous variables corresponding to motorized traffic  volumes and on-street parking, we then allow non-normal taste variation in response to both these exogenous attributes, but originating from a single skew-normal distribution associated with the safety conscious latent variable. Fourth , the multivariate skew-normal (MSN) distribution that we use has properties that make it an ideal one for incorporation into the ICLV model. Finally , the MSN distribution has specific properties that enable the use of Bhat's (2011) maximum approximate composite marginal likelihood (MACML) inference approach for estimation of the resulting skew-normal ICLV (or SN-ICLV) model. This substantially simplifies the estimation approach because the dimensionality of integration in the composite marginal likelihood (CML) function that needs to be maximized to obtain a consistent estimator (under standard regularity conditions) for the SN-ICLV model parameters is independent of the number of latent variables and the number of ordinal indicator variables in the model system. The proposed SN-ICLV model is applied to model bicyclists' route Choice behavior. In this study, two latent variables - pro-bicycle attitude and safety consciousness in the context of traffic crashes - are specified to moderate the effect of route attributes in bicyclist route Choice decisions. A stated preference methodology using a web-based survey of Texas bicyclists provides the route Choice data to implement the SN-ICLV model. The results show that individual-specific observed variables impact route Choice through the latent constructs we develop and not directly, providing substantial support for the ICLV model structure and the specification used in the paper. Importantly, the results show evidence for non-normality in the latent constructs, with the proposed SN-ICLV model soundly rejecting the traditional ICLV model (with normal latent constructs) and a multinomial probit model (with unstructured heterogeneity in the influence of unobserved factors on the sensitivity to route attributes) based on data fit considerations.  Further, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume,  absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route. REFERENCES Azzalini, A., Dalla Valle, A., 1996. The multivariate skew-normal distribution. Biometrica 83(4), 715-726. Bhat, C.R., 2011. The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response Choice models. Transportation Research Part B 45(7), 923-939. Lee, S., McLachlan, G.J., 2013. Finite mixtures of multivariate skew t -distributions: Some recent and new results. Statistics and Computing 24(2), 181-202. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif";}

  • Introducing Non-Normality of Latent Psychological Constructs in Choice Modeling with an Application to Bicyclist Route Choice
    2015
    Co-Authors: Chandra R Bhat, Subodh Dubey, Kai Nagel
    Abstract:

    In the current paper, the authors propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated Choice and latent variable (ICLV) model system. The proposed skew normal (SN)-ICLV model is applied to model bicyclists’ route Choice behavior using a web-based survey of Texas bicyclists.

  • Introducing non-normality of latent psychological constructs in Choice Modeling with an application to bicyclist route Choice
    Transportation Research Part B: Methodological, 2015
    Co-Authors: Chandra R Bhat, Subodh Dubey, Kai Nagel
    Abstract:

    Abstract In the current paper, we propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated Choice and latent variable (ICLV) model system. The multivariate skew-normal (MSN) distribution that we use is tractable, parsimonious in parameters that regulate the distribution and its skewness, and includes the normal distribution as a special interior point case (this allows for testing with the traditional ICLV model). Our procedure to accommodate non-normality in the psychological constructs exploits the latent factor structure of the ICLV model, and is a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables in a multivariate system through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Taste variations (i.e., heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. The resulting model we develop is suitable for estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The proposed model is applied to model bicyclists’ route Choice behavior using a web-based survey of Texas bicyclists. The results reveal evidence for non-normality in the latent constructs. From a substantive point of view, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume, absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route.

  • a new estimation approach to integrate latent psychological constructs in Choice Modeling
    Transportation Research Part B-methodological, 2014
    Co-Authors: Chandra R Bhat, Subodh K Dubey
    Abstract:

    In the current paper, we propose a new multinomial probit-based model formulation for integrated Choice and latent variable (ICLV) models, which, as we show in the paper, has several important advantages relative to the traditional logit kernel-based ICLV formulation. Combining this MNP-based ICLV model formulation with Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach resolves the specification and estimation challenges that are typically encountered with the traditional ICLV formulation estimated using simulation approaches. Our proposed approach can provide very substantial computational time advantages, because the dimensionality of integration in the log-likelihood function is independent of the number of latent variables. Further, our proposed approach easily accommodates ordinal indicators for the latent variables, as well as combinations of ordinal and continuous response indicators. The approach can be extended in a relatively straightforward fashion to also include nominal indicator variables. A simulation exercise in the virtual context of travel mode Choice shows that the MACML inference approach is very effective at recovering parameters. The time for convergence is of the order of 30–80min for sample sizes ranging from 500 observations to 2000 observations, in contrast to much longer times for convergence experienced in typical ICLV model estimations.

Stefan Radtke - One of the best experts on this subject based on the ideXlab platform.

  • business models and programming Choice digital video recorders shaping the tv industry
    Americas Conference on Information Systems, 2005
    Co-Authors: Claudia Loebbecke, Stefan Radtke
    Abstract:

    This paper focuses on the influence that Digital Video Recorders (DVRs) are expected to have on the business models and in particular the programming Choice of TV stations and cable / satellite TV service providers. After presenting the theoretical underpinnings with respect to programming Choice Modeling for the TV industry, this work examines DVRs in detail covering their main product features, impacts on viewer behavior, and the current roll-out status. This paper then investigates how these DVR features impact earlier insights from programming Choice Modeling and how they lead to changed business models for TV stations. The paper concludes with a brief summary and a critical outlook for a TV industry business model.

  • AMCIS - Business Models and Programming Choice: Digital Video Recorders Shaping the TV Industry
    2005
    Co-Authors: Claudia Loebbecke, Stefan Radtke
    Abstract:

    This paper focuses on the influence that Digital Video Recorders (DVRs) are expected to have on the business models and in particular the programming Choice of TV stations and cable / satellite TV service providers. After presenting the theoretical underpinnings with respect to programming Choice Modeling for the TV industry, this work examines DVRs in detail covering their main product features, impacts on viewer behavior, and the current roll-out status. This paper then investigates how these DVR features impact earlier insights from programming Choice Modeling and how they lead to changed business models for TV stations. The paper concludes with a brief summary and a critical outlook for a TV industry business model.

Carlo Giacomo Prato - One of the best experts on this subject based on the ideXlab platform.

  • Methodological transferability in route Choice Modeling
    Transportation Research Part B: Methodological, 2009
    Co-Authors: Shlomo Bekhor, Carlo Giacomo Prato
    Abstract:

    The search for the shortest path constitutes the common practice in actual traffic studies, as this simplistic route Choice model enables the universal implementation of traffic assignment and simulation procedures to every network configuration. The literature illustrates the large efforts in trying to move forward from this simplistic approach, the limited attempts in Modeling route Choice behavior from revealed preference data, and the nonexistent endeavor in investigating the transferability of more realistic path generation techniques and route Choice models. This paper introduces a test to analyze the transferability of path generation techniques that is based on a newly defined efficiency index for the evaluation of their "cost-effectiveness". Then, equality of model estimates is tested to examine the transferability of route Choice models, based on a methodology normally used in the estimation of models with mixed data (typically revealed and stated preference data) and on an existing transferability test statistic commonly used in mode Choice Modeling. Lastly, an experiment is presented to illustrate the implementation of the transferability tests, based on revealed preference data from two different case studies. Experiment results show that path generation techniques are totally transferable at the model specification level and partially transferable at the model parameter level, and that transferability is generally verified when parameters optimized for a larger network are successfully applied to a smaller network. Experiment results also show that not all route Choice models are transferable at the model specification level, and none are transferable at the model parameter level.

  • route Choice Modeling past present and future research directions
    Journal of choice modelling, 2009
    Co-Authors: Carlo Giacomo Prato
    Abstract:

    Modeling route Choice behavior is problematic, but essential to appraise travelers' perceptions of route characteristics, to forecast travelers' behavior under hypothetical scenarios, to predict future traffic conditions on transportation networks and to understand travelers' reaction and adaptation to sources of information. This paper reviews the state of the art in the analysis of route Choice behavior within the discrete Choice Modeling framework. The review covers both Choice set generation and Choice process, since present research directions show growing interest in understanding the role of Choice set size and composition on model estimation and flow prediction, while past research directions illustrate larger efforts toward the enhancement of stochastic route Choice models rather than toward the development of realistic Choice set generation methods. This paper also envisions future research directions toward the improvement in amount and quality of collected data, the consideration of the latent nature of the set of alternatives, the definition of route relevance and Choice set efficiency measures, the specification of models able to contextually account for taste heterogeneity and substitution patterns, and the adoption of random constraint approaches to represent jointly Choice set formation and Choice process.

Emma Frejinger - One of the best experts on this subject based on the ideXlab platform.

  • bike route Choice Modeling using gps data without Choice sets of paths
    Transportation Research Part C-emerging Technologies, 2017
    Co-Authors: Maelle Zimmermann, Tien Mai, Emma Frejinger
    Abstract:

    Abstract Concerned by the nuisances of motorized travel on urban life, policy makers are faced with the challenge of making cycling a more attractive alternative for everyday transportation. Route Choice models can help achieve this objective by gaining insights into the trade-offs cyclists make when choosing their routes and by allowing the effect of infrastructure improvements to be analyzed. We estimate a link-based bike route Choice model from a sample of GPS observations in the city of Eugene on a network comprising over 40,000 links. The so-called recursive logit (RL) model (Fosgerau et al., 2013) does not require to sample any Choice set of paths. We show the advantages of this approach in the context of prediction by focusing on two applications of the model: link flows and accessibility measures. Compared to the path-based approach which requires to generate Choice sets, the RL model proves to make significant gains in computational time and to avoid paradoxical accessibility measure results discussed in previous works, e.g. Nassir et al. (2014).

  • sampling of alternatives for route Choice Modeling
    Transportation Research Part B-methodological, 2009
    Co-Authors: Emma Frejinger, Michel Bierlaire, Moshe Benakiva
    Abstract:

    This paper presents a new paradigm for Choice set generation in the context of route Choice model estimation. We assume that the Choice sets contain all paths connecting each origin-destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm. Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route Choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full Choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS. Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.

  • route Choice Modeling with network free data
    Transportation Research Part C-emerging Technologies, 2008
    Co-Authors: Michel Bierlaire, Emma Frejinger
    Abstract:

    Route Choice models arc difficult to design and to estimate for various reasons. In this paper we focus on issues related to data. Indeed, real data in its original format are not related to the ne ...

  • Three challenges in route Choice Modeling
    2007
    Co-Authors: Michel Bierlaire, Emma Frejinger
    Abstract:

    Route Choice models play an important role in many aspects of transportation analysis, when the usual shortest path paradigm is too unrealistic (real-time information systems, traffic simulation, etc.). Developing operationnal route Choice models involves various challenges. In this talk, we will consider 3 of them: - data collection, - the large size of the Choice set, - the high correlation among the alternatives. For each of them, we will identify the difficulties, and present recent research results developed at EPFL.

Claudia Loebbecke - One of the best experts on this subject based on the ideXlab platform.

  • business models and programming Choice digital video recorders shaping the tv industry
    Americas Conference on Information Systems, 2005
    Co-Authors: Claudia Loebbecke, Stefan Radtke
    Abstract:

    This paper focuses on the influence that Digital Video Recorders (DVRs) are expected to have on the business models and in particular the programming Choice of TV stations and cable / satellite TV service providers. After presenting the theoretical underpinnings with respect to programming Choice Modeling for the TV industry, this work examines DVRs in detail covering their main product features, impacts on viewer behavior, and the current roll-out status. This paper then investigates how these DVR features impact earlier insights from programming Choice Modeling and how they lead to changed business models for TV stations. The paper concludes with a brief summary and a critical outlook for a TV industry business model.

  • AMCIS - Business Models and Programming Choice: Digital Video Recorders Shaping the TV Industry
    2005
    Co-Authors: Claudia Loebbecke, Stefan Radtke
    Abstract:

    This paper focuses on the influence that Digital Video Recorders (DVRs) are expected to have on the business models and in particular the programming Choice of TV stations and cable / satellite TV service providers. After presenting the theoretical underpinnings with respect to programming Choice Modeling for the TV industry, this work examines DVRs in detail covering their main product features, impacts on viewer behavior, and the current roll-out status. This paper then investigates how these DVR features impact earlier insights from programming Choice Modeling and how they lead to changed business models for TV stations. The paper concludes with a brief summary and a critical outlook for a TV industry business model.