Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice

Bhat, Chandra R.; Dubey, Subodh; Nagel, Kai

doi:10.1016/j.trb.2015.04.005

Cited by 39 publications

(15 citation statements)

References 55 publications

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“…Excellent literature reviews of modeling the disaggregate demand for cycling are provided in Sener, et al (2009), Heinen, et al (2010, Li et al (2013), Fernández-Heredia et al (2013), and Maldonado-Hinajeros et al (2014). In particular, Fernández-Heredia et al (2014), Habib et al (2014) and Maldonado-Hinajeros et al (2014) discuss the integration of discrete choice models of cycling decisions with subjective (latent) factors, using hybrid choice models Ben-Akiva et al, 2002; see also Bhat et al, 2015 andKamargianni et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

Motoaki

Daziano

2015

Transportation Research Part A: Policy and Practice

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In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices.

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Section: Introductionmentioning

confidence: 99%

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

Motoaki

Daziano

2015

Transportation Research Part A: Policy and Practice

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“…Since random taste variations and error term heterogeneity are confounded (Brownstone and Train, 1998), the mixed MNP specification can be viewed as an indirect utility with a nonrandom index function and a skew-normal error term. Bhat et al (2015) and Bhat et al (2017) have also used the skew-normal distribution to account for non-normality in latent constructs within an integrated choice and latent variable model (Ben-Akiva et al, 2002) and in the error kernel of an ordered response model, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Readers can refer to Varin and Vidoni (2005) and Varin et al (2011) for a detailed discussion on the CML approach and Bhat (2014) for its derivation and application in the context of discrete choice models. Apart from well-established asymptotic properties (Bhat, 2014), Bhat and co-authors have tested finite sample properties of CML by applying it to complex econometric models and have observed satisfactory results (Bhat and Sidharthan, 2012;Bhat et al, 2015Bhat et al, , 2017.…”

Section: Composite Marginal Likelihood Approachmentioning

confidence: 99%

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

Dubey

Bansal

Daziano

et al. 2020

Transportation Research Part B: Methodological

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In multinomial response models, idiosyncratic variations in the indirect utility are generally modeled using Gumbel or normal distributions. This study makes a strong case to substitute these thin-tailed distributions with a t-distribution. First, we demonstrate that a model with a t-distributed error kernel better estimates and predicts preferences, especially in class-imbalanced datasets. Our proposed specification also implicitly accounts for decision-uncertainty behavior, i.e. the degree of certainty that decision-makers hold in their choices relative to the variation in the indirect utility of any alternative. Second -after applying a t-distributed error kernel in a multinomial response model for the first time -we extend this specification to a generalized continuous-multinomial (GCM) model and derive its full-information maximum likelihood estimation procedure. The likelihood involves an open-form expression of the cumulative density function of the multivariate t-distribution, which we propose to compute using a combination of the composite marginal likelihood method and the separation-of-variables approach. Third, we establish finite sample properties of the GCM model with a t-distributed error kernel (GCM-t) and highlight its superiority over the GCM model with a normally-distributed error kernel (GCM-N) in a Monte Carlo study. Finally, we compare GCM-t and GCM-N in an empirical setting related to preferences for electric vehicles (EVs). We observe that accounting for decision-uncertainty behavior in GCM-t results in lower elasticity estimates and a higher willingness to pay for improving the EV attributes than those of the GCM-N model. These differences are relevant in making policies to expedite the adoption of EVs.

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“…In the last decade and a half, ICLV models have been increasingly applied in many fields to obtain a deeper understanding of the decision process of individuals [19,25,[28][29][30]. Within the field of transportation, ICLV models have been deployed to investigate travel mode choice [31,32], route choice [33], shared-ride transportation service choice [34], flight choice [35], and so on. All the same, the reviewed literature shows that the ICLV model has not been applied to investigate the choice of transportation ticketing channels.…”

Section: Model On Choicementioning

confidence: 99%

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

Jia

Zhang

et al. 2019

Journal of Advanced Transportation

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Buses represent the main mode for intercity passenger transportation in China. In recent years, a multichannel ticketing strategy has been widely employed in the bus passenger transportation industry. However, the mechanisms and key drivers of the channels through which bus passengers purchase tickets are underexplored. Thus, the aim of this study is to empirically apply an integrated choice and latent variable (ICLV) approach to analyze ticketing channel choice behavior and the heterogeneous preferences of bus passengers. The variables incorporated in the model include the socioeconomic characteristics of passengers, trip attributes, and latent attitudes with 12 ordinal indicators. Based on the data of 1800 participants collected from the city of Beijing, China, this study develops a ticketing channel choice ICLV model merging a discrete choice model with a structural equation model. The key factors that affect the channel preference are further discussed through a comparison with a conventional multinomial logit (MNL) model. The results reveal that the three attitudinal variables have a significant influence on ticketing channel choice. Furthermore, this study indicates that perceptual differences exist due to various socioeconomic and trip characteristics. Personal privacy is a major obstacle that prevents passengers from choosing online channels, especially for older passengers and those with lower education.

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Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice

Cited by 39 publications

References 55 publications

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

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