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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]

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]

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]