Ricardo A. Daziano

Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model

In this article we develop, implement and apply a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns. Our study has two main contributions. The first is the feasibility of the Bayesian estimator we derive. Whereas classical estimation of HCMs is fairly complex, we show that the Bayesian approach for HCMs is methodologically easier to implement than simulated maximum likelihood because the inclusion of latent variables translates into adding independent ordinary regressions; we also find that, using the Bayesian estimates, forecasting and deriving confidence intervals for willingness to pay measures is straightforward. The second is the capacity of HCMs to adapt to practical situations. Our empirical results coincide with a priori expectations, namely that environmentally-conscious consumers are willing to pay more for low-emission vehicles. The model outperforms standard discrete choice models because it not only incorporates pro-environmental preferences but also provides tools to build a profile of environmentally-conscious consumers.

Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator

In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem and provides results that are very similar to maximum simulated likelihood estimates. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator, we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then we define a base scenario of vehicle attributes that aims to represent an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies.

Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice

In this paper, we review both the fundamentals and the expansion of computational Bayesian econometrics and statistics applied to transportation modeling problems in road safety analysis and travel behavior. Whereas for analyzing accident risk in transportation networks there has been a significant increase in the application of hierarchical Bayes methods, in transportation choice modeling, the use of Bayes estimators is rather scarce. We thus provide a general discussion of the benefits of using Bayesian Markov chain Monte Carlo methods to simulate answers to the problems of point and interval estimation and forecasting, including the use of the simulated posterior for building predictive distributions and constructing credible intervals for measures such as the value of time. Although there is the general idea that going Bayesian is just another way of finding an equivalent to frequentist results, in practice Bayes estimators have the potential of outperforming frequentist estimators and, at the same time, may offer more information. Additionally, Bayesian inference is particularly interesting for small samples and weakly identified models.

Assessing Goodness of Fit of Hybrid Choice Models

Recent research in travel behavior has contributed numerous technical developments for the estimation of discrete choice models with latent attributes, including the hybrid choice model (HCM). However, assessment of goodness of fit, reliability, validity, and predictive capacities of the joint model remain open research questions. The HCM is a special form of structural equation modeling (SEM). Several goodness-of-fit indexes are all in standard use in psychometric SEM. In this paper, the validity of these indexes is examined for the HCM case. Behavior of SEM fit assessment tools is known in factor analysis (some controversies in this area are reviewed in this paper), but performance of these indexes in the HCM has not been studied. A Monte Carlo study, as well as empirical microdata on bicycle route choice, was used to show that standard SEM fit assessment did not work as expected for the HCM. Important differences were discovered in model fit between the HCM and the multiple indicator multiple cause (MIMIC) model with the same structural and measurement equations for the latent attributes. Sometimes the HCM was rejected when indexes failed to reject the MIMIC structure and vice versa. One of the sources of this divergence was that the measurement equation of the choice kernel did not have an error term; this assumption was nonstandard in SEM. Until a uniform method for measuring the HCM goodness of fit is found, it is recommended that the chi-square test be used for the MIMIC component of the joint model.

Comparison of Parametric and Seminonparametric Representations of Unobserved Taste Heterogeneity in Discrete Choice

The recently derived Logit-Mixed Logit (LML) model captures unobserved preference heterogeneity seminonparametrically as a function of polynomials, step functions, and splines. This study contributes twofold in exploring and extending the usefulness of the LML specification. First, we conduct a Monte-Carlo study to analyze the number of required LML parameters to recover the true distributions, and also compare LML performance -- in terms of accuracy, precision, estimation time, and model fit -- with a Mixed Multinomial Logit specification with Normal heterogeneity (MMNL-N). As expected, LML is able to retrieve the underlying Bimodal-Normal (mixture of two Normal distributions), Lognormal, and Uniform distributions much better than the MMNL-N model. In an empirical case study, we also estimate the willingness to pay (WTP) of German consumers for different vehicle attributes when making choices for alternative-fuel cars. LML is able to capture the bimodal nature of WTP for vehicle attributes, which was not possible to retrieve using the MMNL-N model. LML also appears to be more useful for panel data because the computation time is not affected by the number of choice situations. Second, whereas the original LML formulation assumes all utility parameters to be random, we extend the model to a combination of fixed and random parameters (LML-FR). We further show that the gradient of the LML-FR likelihood loses its convenient properties, leading to a much higher estimation time than that of the original LML specification. In an empirical case study about preferences for alternative-fuel vehicles in China, estimation time increased by a factor of 15-20 when introducing fixed parameters to the LML model. Thus, LML is computationally efficient and better than MMNL-N in retrieving the true distribution of the random taste heterogeneity, but loses its computational efficiency feature if any parameter is assumed to be fixed.

Forecasting Adoption of Ultra-Low-Emission Vehicles Using the GHK Simulator and Bayes Estimates of a Multinomial Probit Model

In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-effcient technologies.

The Proof is in the Picture: The Influence of Imagery and Experience in Perceptions of Hurricane Messaging

AbstractAlthough evidence suggests that photographs can enhance persuasive messaging by offering “proof,” less research considers their utility relative to other visual forms that ostensibly convey more information but more abstractly. Drawing on communication and information processing theory, this study examines the influence of visual features and personal experience variables in a domain with urgent need to better understand their role: hurricane messaging. In a between subjects experiment, residents of New York, New Jersey, and Connecticut (N = 1052) were exposed to a hypothetical hurricane forecast accompanied by a photograph of storm surge inundating a house (indexical image), a map of projected storm surge (iconic image), or no image (control), depending on condition. Results revealed that participants in the indexical condition perceived the greatest risk overall and were more likely to mention evacuation as a behavioral intention than did those in the iconic and control conditions, controlling f...

The Increasing Role of Latent Variables in Modelling Bicycle Mode Choice

ABSTRACT The growing interest in promoting non-motorised active transport has led to an increase in the number of studies to identify the key variables associated with bicycle use, and especially those related to the bicycle mode choice problem. This paper presents a comprehensive survey of the modelling literature on the choice of the bicycle for utilitarian purposes, and summarises and assesses the evolution of the explanatory variables and methodologies used. We review both the evolution of the incorporation of latent variables in bicycle mode choice models and the critical role they play. The chronological evolution of the studies is divided into three stages —initial, intermediate and late — according to the different ways of introducing attitudinal or perceptual indicators and latent variables into the models. Our review shows that the incorporation of latent variables in bicycle choice models has increased in the last decade, with a progressive use of more sophisticated methodologies until the arrival of complex models that explicitly and properly deal with psychological latent variables. In fact, with the use of hybrid choice models, latent variables have nowadays become the core of bicycle mode choice models. Based on our review, a set of questions is proposed as a uniform measurement scale to identify attitudes towards bicycling. Recommendations for future research are also presented.

Analyzing Probit Bayes Estimator for Flexible Covariance Structures in Discrete Choice Modeling

Research in discrete choice modeling in recent decades has devoted an enormous effort to generalizing the distribution of the error term and to developing estimation methods that account for more flexible structures of error heterogeneity. Whereas the multinomial probit model offers a fully flexible covariance matrix, the maximum simulated likelihood estimator is extremely involved. However, Bayesian techniques have the potential to break down the complexity of the estimator. By using a Monte Carlo study, this paper tests the ability of a probit Bayes estimator based on Gibbs sampling to recover different substitution patterns. The results show that it is possible to use the Bayes estimator of a full covariance matrix to recover different covariance structures, even when small samples are used. Thus, the model can identify the true substitution patterns, by avoiding misspecification, even if these patterns are the result of multiple restrictions over the covariance matrix. In fact, the recovery of simpler covariance structures, such as that of the independent and identically distributed and heteroskedastic covariance without correlation, is more accurate than the recovery of more complicated structures, including fully unrestricted substitution patterns.

A framework to integrate mode choice in the design of mobility-on-demand systems

Abstract Mobility-on-Demand (MoD) systems are generally designed and analyzed for a fixed and exogenous demand, but such frameworks fail to answer questions about the impact of these services on the urban transportation system, such as the effect of induced demand and the implications for transit ridership. In this study, we propose a unified framework to design, optimize and analyze MoD operations within a multimodal transportation system where the demand for a travel mode is a function of its level of service. An application of Bayesian optimization (BO) to derive the optimal supply-side MoD parameters (e.g., fleet size and fare) is also illustrated. The proposed framework is calibrated using the taxi demand data in Manhattan, New York. Travel demand is served by public transit and MoD services of varying passenger capacities (1, 4 and 10), and passengers are predicted to choose travel modes according to a mode choice model. This choice model is estimated using stated preference data collected in New York City. The convergence of the multimodal supply-demand system and the superiority of the BO-based optimization method over earlier approaches are established through numerical experiments. We finally consider a policy intervention where the government imposes a tax on the ride-hailing service and illustrate how the proposed framework can quantify the pros and cons of such policies for different stakeholders.