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The COVID-19 pandemic has put a spotlight on the importance of crowd avoidance to understand behavior during a public health emergency. In terms of travel behavior modeling tools, it is not obvious how spatial elements such as crowding should be displayed in discrete choice experiments. In this paper, we argue in favor of using virtual environments. We describe how we have successfully used static virtual environments in two pandemic-related case studies. Extrapolating from these two applications, we discuss how immersive virtual environments could be used in travel behavior research in general, along with the identification of associated methodological challenges.

In-vehicle crowding and travel time are two important factors that determine passenger transportation preferences in public transit. A widely used approach to obtain the relative magnitude of these preferences is the stated choice (SC) survey. However, such imaginary choice situations do not capture automatic cognitive biases during the immersive experience of trips. In a previous study, we used virtual reality (VR) technology to simulate short immersive virtual subway trips with different levels of crowding. We asked participants to indicate the level of pleasantness and estimate the duration of each trip. In this paper, we compare and contrast perceptions of participants in the VR task with preferences in a SC survey taken from the same participants. The SC task consisted of two-alternative choice scenarios, asking for preference between more crowded shorter trips and less crowded longer trips. Discrete choice modeling was used to analyze the SC results. There are two main findings. First, individuals who perceived passenger density more negatively in the SC task also felt more negatively during higher density VR trips. This confirms that hypothetical SC surveys can reflect feelings induced by crowding during more realistic experiences. Secondly, a more crowded VR trip was perceived as longer compared with a less crowded trip, whereas this effect was not reflected in the SC task. It therefore suggests that SC surveys may not be capable of capturing systematic temporal biases induced by crowding. Results shed light on potential caveats of the SC surveys and introduce an avenue for the use of VR in passenger preference research.

Random parameter logit models address unobserved preference heterogeneity in discrete choice analysis. The latent class logit model assumes a discrete heterogeneity distribution, by combining a conditional logit model of economic choices with a multinomial logit (MNL) for stochastic assignment to classes. Whereas point estimation of latent class logit models is widely applied in practice, stochastic assignment of individuals to classes needs further analysis. In this paper we analyze the statistical behavior of six competing class assignment strategies, namely: maximum prior MNL probabilities, class drawn from prior MNL probabilities, maximum posterior assignment, drawn posterior assignment, conditional individual-specific estimates, and conditional individual estimates combined with the Krinsky–Robb method to account for uncertainty. Using both a Monte Carlo study and two empirical case studies, we show that assigning individuals to classes based on maximum MNL probabilities behaves better than randomly drawn classes in market share predictions. However, randomly drawn classes have higher accuracy in predicted class shares. Finally, class assignment based on individual-level conditional estimates that account for the sampling distribution of the assignment parameters shows superior behavior for a larger number of choice occasions per individual.

•Use City of Chicago as a case study.•Examine how trip cost and travel time affect choice between shared and non-shared ride-hail modes.•Binomial logit and latent class multinomial logit models to investigate mode choice.•Novel method linking individual passenger characteristics to ride-hailing trip characteristics.•Individual socio-economic characteristics have greater impact on mode choice than trip cost and travel time do.

ABSTRACTWe formalize one aspect of reliabilityin the context of Mobility-on-Demand (MoD) systems by acknowledging the uncertainty in the pick-up time of these services. This study answers two key questions: i) how the difference between the stated and actual pick-up times affect the propensity of a passenger to choose an MoD service? ii) how an MoD service provider can leverage this information to increase its ridership? We conduct a discrete choice experiment in New York to answer the former question and adopt a micro-simulation-based optimization method to answer the latter question. In our experiments, the ridership of an MoD service could be increased by up to 10% via displaying the predicted wait time strategically.

•Use a unique sample (N = 11,902) from TNC-served areas in the United States.•TNCs are mainly attracting personal vehicle users; small effect on transit demand.•10% of TNC users postponed the purchase of a new car due to availability of TNCs.•Postgraduate TNC drivers who live in metropolitan regions are pro-fuel-efficient-car.•Older TNC users with higher vehicle ownership are less likely to pool rides.

•We analyze a survey applied in the United States to understand the impacts of Covid-19 on attitudes towards microtransit.•Structural equation modeling shows no correlation between pandemic attitudes and interest in microtransit.•Choice modeling showed a higher propensity to use shared mobility during the pandemic correlated with the chances of choosing it in a series of hypothetical scenarios.•A qualitative analysis found no considerable correlation between openended questions related to microtransit and the pandemic.

This paper summarizes the discussion of the workshop “new directions in experimental design”, in which the evolution of experimental designs was discussed in the context of the rapid changes and challenges that the transportation field is experiencing. Because an experimental setting in travel behavior analysis is associated with stated preference methods, the workshop had a clear focus on the design of discrete choice experiments. However, one of the main conclusions is that we need an expanded notion of experiments to achieve diversity in the participation of experimentalists beyond standard stated preference analysts. In this broader, more holistic definition of experiment, workshop participants made clear that good designs need to explicitly address treatments and control, as well as the identification of a good, appropriate baseline for valid hypothesis testing. In fact, the group also discussed the idea of “experiment design of the experiment design” meaning that we should control in a methodical way the parameters of the design so that analysts can assess and formalize design protocols, while also examining the combined use of conventional and new tools.

Due to potentially high initial purchase prices, automation is likely to hit the transportation market as on-demand autonomous taxis for short-term rentals. In this study, welfare measures associated with the use of autonomous taxis were estimated by conducting discrete choice experiments (DCEs) in New York City. Aiming at more realistic choice scenarios, a method for pivot-efficient designs is proposed and tested that exploits the distribution of attribute levels; however, analysis suggests the use of a simpler pivot-efficient design with average attributes of reference alternatives. In our sample New Yorkers were willing to pay on average $3 less per self-driven trip. This reduction in the willingness to pay is coming from the fact that in current conditions not having a driver may be perceived as a nuisance rather than a convenience.

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.

This paper summarizes the discussion of the “caring for the environment workshop”, including the following research challenges in dealing with environmental concerns and preferences related to transportation decisions: adoption of multidisciplinary survey methods that account for dynamics in a broad sense, sensor-based and crowd-sourced energy and environmental inventories for active data collection, and design of effective behavioural interventions where data collection and information provision are integrated aiming at the goal of reducing environmental footprints. In particular, non-traditional tools such as gamification should be explored further as part of the design of the behavioural interventions.

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.

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.

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.

The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.

The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.

In the past decade, a new trend in discrete choice modeling has emerged: psychological factors are explicitly incorporated to enhance the behavioral representation of the choice process. In this context, hybrid models expand on standard choice models by including attitudes and perceptions as latent variables. The complete model is composed of a group of structural equations describing the latent variables in terms of observable exogenous variables and a group of measurement relationships linking latent variables to certain observable indicators. Although the estimation of hybrid models requires the evaluation of complex multidimensional integrals, simulated maximum likelihood is implemented to solve the integrated multiple-equation model. This study empirically evaluates the application of hybrid choice modeling to data from a survey conducted by the Energy and Materials Research Group (Simon Fraser University, 2002 and 2003) of the virtual personal vehicle choices made by Canadian consumers when they are faced with technological innovations. The survey also includes a complete list of indicators that allows the application of a hybrid choice model formulation. It is concluded that the hybrid choice model is genuinely capable of adapting to practical situations by including latent variables among the set of explanatory variables. The incorporation of perceptions and attitudes in this way leads to more realistic models and gives a better description of the profile of consumers and their adoption of new automobile technologies.

Discrete choice models with error structures that are not independent and identically distributed have received enormous attention in the recent literature. A detailed synthetic study tests this type of model in a controlled case. With mixed logit and probit models as the study objects, calibration was implemented with the use of software available on the Internet. The controlled situation was built as a simulation laboratory, which generated databases with known parameters. The effects of various elements were analyzed: number of repetitions of the simulation, number of observations in the database, and how the use of Halton sequences improves the mixed logit calibration. The scale effects on the different models are also discussed. The results obtained in this specific context lead to some recommendations for future users of these powerful modeling tools. In particular, flexible structures require large sample sizes to calibrate the elements of the covariance matrix.

Discrete choice models with error structures that are not independent and identically distributed have received enormous attention in the recent literature. A detailed synthetic study tests this type of model in a controlled case. With mixed logit and probit models as the study objects, calibration was implemented with the use of software available on the Internet. The controlled situation was built as a simulation laboratory, which generated databases with known parameters. The effects of various elements were analyzed: number of repetitions of the simulation, number of observations in the database, and how the use of Halton sequences improves the mixed logit calibration. The scale effects on the different models are also discussed. The results obtained in this specific context lead to some recommendations for future users of these powerful modeling tools. In particular, flexible structures require large sample sizes to calibrate the elements of the covariance matrix.