Search

We present a microeconomic model for time use and consumption for workers with an improved treatment of the (technical) relations between goods and time. In addition to the traditional time and income constraints, an improved set of restrictions involving explicit relations between consumption of goods and time assigned to activities is included in two versions. In each version, a system of equations involving a subset of the consumer's decision variables is obtained, including (1) work time, (2) activities that are assigned more time than the minimum, and (3) goods that are consumed above the minimum. The system cannot be solved explicitly in the endogenous decision variables but is used to set a stochastic system for econometric estimation through maximum likelihood. The models are applied to analyze weekly time use and consumption data from Netherlands for year 2012. The results obtained by this new ``goods and time'' framework are compared with previous research in terms of the value of leisure and the value of work, showing substantial differences in the valuation of time.

Reporting and hypothetical biases are inherent to canonical methods of transportation data collection and had implied that analysis in this field has often neglected aspects that are strong behavioral drivers, such as uncertainty, physical effort or stress. Granular information on these aspects would allow measuring their valuation and/or addressing a pervasive source of endogeneity. Recent advances in miniaturization and data processing, as well as evidence that indicators from biosensors correlate with psychophysiological states and emotions, suggest that there is an opportunity to close this gap by collecting a new type of data from transportation users. This research works on leveraging this opportunity by putting forward, illustrating and testing a methodological framework to incorporate psychophysiological indicators gathered from biosensors into transportation choice behavioral modeling. The proposed framework adapts the integrated choice and latent variable approach by incorporating the psychophysiological responses as additional indicators of a latent psychophysiological state that covariates with utility. For the practical implementation of the proposed framework we also consider a specific form of aggregation of the indicators across time to avoid the curse of dimensionality arising from the unmanageably large number of folds for integration. The proposed framework is illustrated and validated using Monte Carlo simulations. Besides, a prototype field experiment was designed and performed to confirm the validity of three crucial components of the proposed framework: (i) the relation between transportation markers and emotions; (ii) the possibility of measuring those emotions through biosensors installed on travelers, (iii) and the validity of the proposed aggregation needed for practicality. In the experiment, a public transportation user travelled wearing a Printed Circuit Board that integrated tiny biosensors to capture electrodermal activity, heart rate variation, temperature and acceleration. Results provide positive evidence for the research questions, suggesting the convenience of developing larger data collection efforts in the future to take full advantage of the new framework.

Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another, along with a continuous quantity dimension for each chosen alternative. To model such multiple discrete–continuous choices, most multiple discrete–continuous models in the literature use an additively-separable utility function, with the assumption that the marginal utility of one good is independent of the consumption of another good. In this paper, we develop model formulations for multiple discrete–continuous choices that accommodate rich substitution structures and complementarity effects in the consumption patterns, and demonstrate an application of the model to transportation-related expenditures using data drawn from the 2002 Consumer Expenditure (CEX) Survey.

This paper develops a blueprint (complete with matrix notation) to apply Bhat’s (2011) Maximum Approximate Composite Marginal Likelihood (MACML) inference approach for the estimation of cross-sectional as well as panel multiple discrete–continuous probit (MDCP) models. A simulation exercise is undertaken to evaluate the ability of the proposed approach to recover parameters from a cross-sectional MDCP model. The results show that the MACML approach does very well in recovering parameters, as well as appears to accurately capture the curvature of the Hessian of the log-likelihood function. The paper also demonstrates the application of the proposed approach through a study of individuals’ recreational (i.e., long distance leisure) choice among alternative destination locations and the number of trips to each recreational destination location, using data drawn from the 2004 to 2005 Michigan statewide household travel survey.

Multiple-discrete continuous choice models formulated and applied in recent years consider a single linear resource constraint, which, when combined with consumer preferences, determines the optimal consumption point. However, in reality, consumers face multiple resource constraints such as those associated with time, money, and capacity. Ignoring such multiple constraints and instead using a single constraint can, and in general will, lead to poor data fit and inconsistent preference estimation, which can then have a serious negative downstream effect on forecasting and welfare/policy analysis. In this paper, we extend the multiple-discrete continuous extreme value (MDCEV) model to accommodate multiple constraints. The formulation uses a flexible and general utility function form, and is applicable to the case of complete demand systems as well as incomplete demand systems. The proposed MC-MDCEV model is applied to time-use decisions, where individuals are assumed to maximize their utility from time-use in one or more activities subject to monetary and time availability constraints. The sample for the empirical exercise is generated by combining time-use information from the 2008 American Time Use Survey and expenditure records from the 2008 US Consumer Expenditure Survey. The estimation results show that preferences can get severely mis-estimated, and the data fit can degrade substantially, when only a subset of active resource constraints is used.

This paper proposes a reformulation of count models as a special case of generalized ordered-response models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in practice. A composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Arlington, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files between 2003 and 2009, resulting in 1190 intersection-year observations. The results reveal the presence of intersection-specific time-invariant unobserved components influencing crash propensity and a spatial lag structure to characterize spatial dependence. Roadway configuration, approach roadway functional types, traffic control type, total daily entering traffic volumes and the split of volumes between approaches are all important variables in determining crash frequency at intersections.

Identifying the set of available alternatives in a choice process after considering an individual’s bounds or thresholds is a complex process that, in practice, is commonly simplified by assuming exogenous rules in the choice set formation. The Constrained Multinomial Logit (CMNL) model incorporates thresholds in several attributes as a key endogenous process to define the alternatives choice/rejection mechanism. The model allows for the inclusion of multiple constraints and has a closed form. In this paper, we study the estimation of the CMNL model using the maximum likelihood function, develop a methodology to estimate the model overcoming identification problems by an endogenous partition of the sample, and test the model estimation with both synthetic and real data. The CMNL model appears to be suitable for general applications as it presents a significantly better fit than the MNL model under constrained behaviour and replicates the MNL estimates in the unconstrained case. Using mode choice real data, we found significant differences in the values of times and elasticities between compensatory MNL and semi-compensatory CMNL models, which increase as the thresholds on attributes become active.