An Empirical Investigation into the Time-Use and Activity Patterns of Dual-Earner Couples With and Without Young Children

At the time of publication M. Hoklas and C.R. Bhat were at the University of Texas at Austin, while C. Bernardo and R. Paleti were at Parsons Brinckerhoff.This paper examines the time-use patterns of adults in dual-earner households with and without children as a function of several individual and household socio-demographics and employment characteristics. A disaggregate activity purpose classification including both in-home and out-ofhome activity pursuits is used because of the travel demand relevance of out-of-home pursuits, as well as to examine both mobility-related and general time-use related social exclusion and time poverty issues. The study uses the Nested Multiple Discrete Continuous Extreme Value (MDCNEV) model, which recognizes that time-decisions entail the choice of participating in one or more activity purposes along with the amount of time to invest in each chosen activity purpose, and allows generic correlation structures to account for common unobserved factors that might impact the choice of multiple alternatives. The 2010 American Time Use Survey (ATUS) data is used for the empirical analysis. A major finding of the study is that the presence of a child in dual-earner households not only leads to a reduction in in-home non-work activity participation (excluding child care activities) but also a substantially larger decrease in out-ofhome non-work activity participation (excluding child care and shopping activities), suggesting a higher level of mobility-related social exclusion relative to overall time-use social exclusion. To summarize, the results in the paper underscore the importance of considering household structure in activity-based travel demand models, as well as re-designing work policies in the United States to facilitate a reduction in work-family conflict in dual-earner families.Civil, Architectural, and Environmental Engineerin

An Empirical Analysis of Children's After School Out-of-Home Activity-Location Engagement Patterns and Time Allocation

At the time of publication R. Paleti and C.R. Bhat were at the University of Texas at Austin; and R.B. Copperman was at Cambridge Systematics, Inc.Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children's travel and activity participation during the post-school period have direct implication for adults' activity-travel patterns. A better understanding of children's after school activity-travel patterns and the linkages between parents and children's activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. Specifically, this research effort utilizes a multinomial logit model to analyze children's post-school location patterns, and employs a multiple discrete-continuous extreme value (MDCEV) model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others' activity-travel pattern characteristics impact children's after school activity engagement patterns.Civil, Architectural, and Environmental Engineerin

The Composite Marginal Likelihood (CML) Estimation of Panel Ordered-Response Models

At the time of publication Rajesh Paleti was at Parsons Brinckerhoff and Chandra R. Bhat was at the University of Texas at Austin.In the context of panel ordered-response structures, the current paper compares the performance of the maximum-simulated likelihood (MSL) inference approach and the composite marginal likelihood (CML) inference approach. The panel structures considered include the pure random coefficients (RC) model with no autoregressive error component, as well as the more general case of random coefficients combined with an autoregressive error component. The ability of the MSL and CML approaches to recover the true parameters is examined using simulated datasets. The results indicate that the performances of the MSL approach (with 150 scrambled and randomized Halton draws) and the simulation-free CML approach are of about the same order in all panel structures in terms of the absolute percentage bias (APB) of the parameters and econometric efficiency. However, the simulation-free CML approach exhibits no convergence problems of the type that affect the MSL approach. At the same time, the CML approach is about 5-12 times faster than the MSL approach for the simple random coefficients panel structure, and about 100 times faster than the MSL approach when an autoregressive error component is added. As the number of random coefficients increases, or if higher order autoregressive error structures are considered, one can expect even higher computational efficiency factors for the CML over the MSL approach. These results are promising for the use of the CML method for the quick, accurate, and practical estimation of panel ordered-response models with flexible and rich stochastic specifications.Civil, Architectural, and Environmental Engineerin

Institutional Capacity Building for Travel Demand Modeling Development at Metropolitan Planning Organizations (MPOs): A Human Resource Perspective

At the time of publication Chandra R. Bhat was at the University of Texas at Austin and Sriram Narayanamoorthy and Rajesh Paleti were at Parsons Brinckerhoff.In this paper, we discuss possible changes to the operation and functioning of MPOs that can potentially increase their institutional capacity and efficiency in the Travel Demand Model (TDM) development process, as well as their overall competence level.Civil, Architectural, and Environmental Engineerin

A Spatial Generalized Ordered Response Model to Examine Highway Crash Injury Severity

This paper proposes a flexible econometric structure for injury severity analysis at the level of individual crashes that recognizes the ordinal nature of injury severity categories, allows unobserved heterogeneity in the effects of contributing factors, as well as accommodates spatial dependencies in the injury severity levels experienced in crashes that occur close to one another in space. The modeling framework is applied to analyze the injury severity sustained in crashes occurring on highway road segments in Austin, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files from 2009 and includes a variety of crash characteristics, highway design attributes, driver and vehicle characteristics, and environmental factors. The results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. The most important determinants of injury severity on highways, according to our results, are (1) whether any vehicle occupant is ejected, (2) whether collision type is head-on, (3) whether any vehicle involved in the crash overturned, (4) whether any vehicle occupant is unrestrained by a seat-belt, and (5) whether a commercial truck is involved.Civil, Architectural, and Environmental Engineerin

The Joint Analysis of Injury Severity of Drivers in Two-Vehicle Crashes Accommodating Seat Belt Use Endogeneity

At the time of publication K.A. Abay was at the University of Copenhagen, R. Paleti was at Parsons Brinckerhoff, and C.R. Bhat was at the University of Texas at Austin.The current study contributes to the existing injury severity modeling literature by developing a multivariate probit model of injury severity and seat belt use decisions of both drivers involved in two-vehicle crashes. The modeling approach enables the joint modeling of the injury severity of multiple individuals involved in a crash, while also recognizing the endogeneity of seat belt use in predicting injury severity levels as well as accommodating unobserved heterogeneity in the effects of variables. The proposed model is applied to analyze the injury severity of drivers involved in two-vehicle road crashes in Denmark. The empirical analysis provides strong support for the notion that people offset the restraint benefits of seat belt use by driving more aggressively. Also, men and those individuals driving heavy vehicles have a lower injury risk than women and those driving lighter vehicles, respectively. At the same time, men and individuals driving heavy vehicles pose more of a danger to other drivers on the roadway when involved in a crash. Other important determinants of injury severity include speed limit on roadways where crash occurs, the presence (or absence) of center dividers (median barriers), and whether the crash involves a head-on collision. These and other results are discussed, along with implications for countermeasures to reduce injury severities in crashes. The analysis also underscores the importance of considering injury severity at a crash level, while accommodating seat belt endogeneity effects and unobserved heterogeneity effects.Civil, Architectural, and Environmental Engineerin

A New Econometric Approach to Multivariate Count Data Modeling

At the time of publication Chandra R. Bhat and Marisol Castro were at the University of Texas at Austin, and Rajesh Paleti was at Parsons Brinckerhoff.In the current paper, we propose a modeling framework to explicitly link a count data model with an event type multinomial choice model. The proposed framework uses a multinomial probit kernel for the event type choice model and introduces unobserved heterogeneity in both the count and discrete choice components. Additionally, this paper establishes several new results regarding the distribution of the maximum of multivariate normally distributed variables, which form the basis to embed the multinomial probit model within a joint modeling system for multivariate count data. The model is applied for analyzing out-of-home non-work episodes pursued by workers, using data from the National Household Travel Survey.Civil, Architectural, and Environmental Engineerin

An Analysis of the Factors Influencing Differences in Survey-Reported and GPS-Recorded Trips

At the time of publication S.G. Bricka was Texas Transportation Institute, S. Sen was at NuStats, and R. Paleti and C.R. Bhat were at the University of Texas at Austin.Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional surveyreported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPSrecorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-hour period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.Civil, Architectural, and Environmental Engineerin

A Latent Variable Representation of Count Data Models to Accommodate Spatial and Temporal Dependence: Application to Predicting Crash Frequency at Intersections

This paper proposes a reformulation of count models as a special case of generalized orderedresponse 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 1,190 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.Civil, Architectural, and Environmental Engineerin

On Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator

At the time of publication Rajesh Paleti was at Parsons Brinckerhoff, Chandra R. Bhat was at the University of Texas at Austin, Ram M. Pendyala was at Arizona State University, Konstadinos G. Goulias was at the University of California Santa Barbara, Thomas J. Adler was at Resource Systems Group, Inc., and Aniss Bahreinian was at California Energy Commission.The carbon footprint of personal travel is dependent on the composition of the vehicle fleet and the extent to which vehicles of different types are utilized. Transportation model systems have previously not explicitly incorporated the ability to forecast vehicle fleet composition and utilization patterns of households in a region. In the absence of such modeling capability, it is difficult to predict the energy and environmental impacts of alternative policy, market, and technology scenarios in the future. This paper describes the application of a comprehensive vehicle fleet composition and evolution model system that is capable of taking a base year vehicle fleet and evolving it over time in annual time steps through the events of vehicle disposal, replacement, and acquisition. Results of the scenario application exercise documented in this paper demonstrate the efficacy of the model system.Civil, Architectural, and Environmental Engineerin