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Time sometimes feels like it is flying by or slowing down. Previous research indicates objective number of items, subjective affect, and heart rate all can influence the experience of time. While these factors are usually tested in isolation with simple stimuli in the laboratory, here we examined them together in the ecological context of a virtual subway ride. We hypothesized that subjective affective experience associated with objective crowding lengthens subjective trip duration. Participants (N = 41) experienced short (1–2 min) immersive virtual reality subway trips with different levels of public crowding. Consistent with the immersive nature of decreased interpersonal virtual space, increased crowding decreased pleasantness and increased the unpleasantness of a trip. Virtual crowding also lengthened perceived trip duration. The presence of one additional person per square meter of the train significantly increased perceived travel time by an average of 1.8 s. Degree of pleasant relative to unpleasant affect mediated why crowded trips felt longer. Independently of crowding and affect, heart rate changes were related to experienced trip time. These results demonstrate socioemotional regulation of the experience of time and that effects of social crowding on perception and affect can be reliably created during a solitary virtual experience. This study demonstrates a novel use of Virtual Reality technology for testing psychological theories in ecologically valid and highly controlled settings. [ABSTRACT FROM AUTHOR]

This study aims to understand how self-assessed health status relates to preferences for cycling infrastructure. An integrated latent class and latent variable choice model is fitted using responses to a stated preference experiment from a panel of New York City residents (N = 801). Estimates show that people with stated good physical health tend to have preference parameters similar to those of experienced cyclists. This result means that the provision of cycling infrastructure with the purpose of attracting non-cyclists also has the potential of attracting those with worse health outcomes. This result suggests a double benefit coming from car use reduction and lower health spending. [ABSTRACT FROM AUTHOR]

We compare responses from an online survey among 700 customers of transportation network companies (TNC) in Boston and Philadelphia to investigate TNC's impact on vehicle ownership, trip making, and mode choice. We first use a qualitative comparative analysis to examine changes in respondents' travel behavior and vehicle ownership after adopting TNC. We then use a random parameter logit regression analysis to investigate customers' preferences between transit and TNC based on a choice experiment. We find that in both cities, TNC allows customers, including those who currently do not own a car, to either delay purchasing a car or forgo a car altogether. TNC enables customers across income levels to take trips that they otherwise would not have taken. Meanwhile, TNC substitutes for more than complementing transit. The random parameter logit analysis indicates that when choosing between TNC and transit, individuals in both cities consider waiting time and overall travel time for transit to be more burdensome than those for TNC. Bostonians perceive the time spent walking to and from transit to be less burdensome, and the time spent traveling in vehicle to be more burdensome than do Philadelphians. Differences in built environment, mode share within transit systems, and income likely contribute to respondents' different values of time between the two cities. Our paper is the first to compare individual trade-off between transit and TNC in two cities with different urban settings and transit services. The findings have implications on transit service planning, station area improvements, parking regulations, and traffic management. [ABSTRACT FROM AUTHOR]

Various measures have been proposed and validated to assess environmental motivation and explain peoples' consumer behavior. However, most of the measures are rather complex, sometimes comprising dozens of items. In order to overcome the associated response burden, the goal of our research is to validate a much simpler measure of environmental motivation, namely the measure of Climate Change-Stage of Change. To do so we analyze data from a discrete choice experiment in which drivers decide to purchase a car with different levels of CO2 emissions and we also measure their environmental motivation with three alternative measures. The results show that environmental motivation assessed with Climate Change-Stage of Change explains the choices in the experiment as well as with more complex measures. Our findings have substantial implications for researchers as they may be able to assess climate-relevant motivation – a significant factor for many consumer choices – with a single question. [ABSTRACT FROM AUTHOR]

Maximum simulated likelihood estimation of mixed multinomial logit models requires evaluation of a multidimensional integral. Quasi-Monte Carlo (QMC) methods such as Halton sequences and modified Latin hypercube sampling are workhorse methods for integral approximation. Earlier studies explored the potential of sparse grid quadrature (SGQ), but SGQ suffers from negative weights. As an alternative to QMC and SGQ, we looked into the recently developed designed quadrature (DQ) method. DQ requires fewer nodes to get the same level of accuracy as QMC and SGQ, is as easy to implement, ensures positivity of weights, and can be created on any general polynomial space. We benchmarked DQ against QMC in a Monte Carlo and an empirical study. DQ outperformed QMC in all considered scenarios, is practice ready, and has potential to become the workhorse method for integral approximation. [ABSTRACT FROM AUTHOR]

Background: Using choice microdata (N=2723) across the USA, this paper analyzes elicited acceptance of hypothetical COVID-19 vaccines. Methods: The hypothetical vaccines in a choice experiment were described in terms of effectiveness, days for antibodies to develop, duration of protection, risk of both mild and severe side effects, which health agency mainly supports the vaccine, country of origin, and when the vaccine was developed. Out-of-pocket cost was also considered as characteristic of the vaccines to derive welfare measures. Results: All vaccine attributes had expected signs with significant estimates. Vaccines developed in the USA and the UK were preferred to a hypothetical German vaccine, whereas a Chinese origin was very negatively perceived. Since the choice scenarios also gave the option to opt out from taking the vaccine, odds ratios were derived to characterize the segments that are more and less likely to accept vaccination. More likely to opt out were found to be those who stated to be against vaccination in general, African Americans, individuals without health insurance, and older people. Males, democrats, those who took the flu vaccine appear as more willing to accept vaccination. Conclusions: Estimates of the fitted choice models in this study are informative for current and future immunization programs. [ABSTRACT FROM AUTHOR]

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We formalize one aspect of reliability in 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. [ABSTRACT FROM AUTHOR]

Research suggests that hurricane-related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals' distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York ( n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual-level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences. [ABSTRACT FROM AUTHOR]

Although 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 5 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 for individual differences (gender, state of residence, etc.). Moreover, risk perception was greatest among residents in the indexical condition reporting fewer personal impacts of hurricanes, suggesting a moderating effect of hurricane experience on risk judgment but not on behavioral intention. Consistent with a dual-processmodel perspective, when exposed to an image of an identifiable "victim," participants with less direct experience may have employed an affect heuristic, resulting in heightened risk perceptions. Practically speaking, using evocative photographs as proof may be preferable to a map or text-only approachwhenwarning public audiences of a given hazard, but ethical issues and empirical questions remain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Users of transportation systems face economic decisions that involve cash flows in different points in time. In making these intertemporal choices, users tradeoff between immediate and future benefits and costs. For example, battery electric vehicles require a higher investment but offer important energy savings that may pay off. Using an appropriate discount rate, future costs are transformed into a present equivalent that can be directly compared with upfront expenses or known prices. In this paper we review different methods for measuring individual discount rates that have been used in the literature to discuss, in particular, the findings of the 'energy paradox' or undervaluation of future savings (making more difficult the adoption of energy efficient vehicles). We identify two main methods of treating discount rates in discrete choice models, namely endogenous and exogenous discounting. Exogenous discounting addresses some problems of the endogenous discounting method (attribute correlation, endogeneity, and imposition of rational evaluation of future costs), but lacks correct treatment of unobserved heterogeneity. We also review experimental elicitation of intertemporal preferences that accounts for heterogeneous discounting. In particular, we tested a modified version of a reward-over-time choice experiment that can be easily incorporated into transportation surveys. In addition, we compared the experimental discount rates with those obtained from a contingent valuation exercise. The continent valuation exercise not only provided a much higher mean for the discount rate (13.93 % was the mean experimental discount rate, whereas the contingent valuation mean was 93.9 %), but also the level of variability among respondents was unexpectedly high (6.4-267 %). [ABSTRACT FROM AUTHOR]

In this article we evaluate the impacts of travel demand management strategies to reduce the share of automobiles as a commuting mode by using stated preference data. The analysis is carried out on university members in Quebec City. We measure the impact of travel time and cost as well as attitudes toward automobiles, transit, and the environment. We find elasticities with respect to time and cost that are low, implying that large changes in the characteristics of commuting modes are required to have a noticeable impact. We find, however, that combining several policy interventions is more effective. [ABSTRACT FROM AUTHOR]

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-dutyvehicle 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. [ABSTRACT FROM AUTHOR]

In this paper we discuss the specification, covariance structure, estimation, identification, and point-estimate analysis of a logit model with endogenous latent attributes that avoids problems of inconsistency. We show first that the total error term induced by the stochastic latent attributes is heteroskedastic and nonindependent. In addition, we show that the exact identification conditions support the two-stage analysis found in much current work. Second, we set up a Monte Carlo experiment where we compare the finite-sample performance of the point estimates of two alternative methods of estimation, namely frequentist full information maximum simulated likelihood and Bayesian Metropolis Hastings-within-Gibbs sampling. The Monte Carlo study represents a virtual case of travel mode choice. Even though the two estimation methods we analyze are based on different philosophies, both the frequentist and Bayesian methods provide estimators that are asymptotically equivalent. Our results show that both estimators are feasible and offer comparable results with a large enough sample size. However, the Bayesian point estimates outperform maximum likelihood in terms of accuracy, statistical significance, and efficiency when the sample size is low. [ABSTRACT FROM AUTHOR]

Indications of people's environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone's level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7% to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy. [ABSTRACT FROM AUTHOR]