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21.
Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning
professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However,
such models need to take into account self-selection effects in residential location choice, wherein households choose to
reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon,
well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use
and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved
factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents
a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle
miles of travel) using a copula-based framework that facilitates the estimation of joint equations systems with error dependence
structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived
from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency
among the choice dimensions and that self-selection effects cannot be ignored when modeling land use-travel behavior interactions. 相似文献
22.
Astroza Sebastian Garikapati Venu M. Pendyala Ram M. Bhat Chandra R. Mokhtarian Patricia L. 《Transportation》2019,46(5):1755-1784
Transportation - Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict... 相似文献
23.
Nazneen Ferdous Naveen Eluru Chandra R. Bhat Italo Meloni 《Transportation Research Part B: Methodological》2010,44(8-9):922-943
This paper proposes a multivariate ordered-response system framework to model the interactions in non-work activity episode decisions across household and non-household members at the level of activity generation. Such interactions in activity decisions across household and non-household members are important to consider for accurate activity-travel pattern modeling and policy evaluation. The econometric challenge in estimating a multivariate ordered-response system with a large number of categories is that traditional classical and Bayesian simulation techniques become saddled with convergence problems and imprecision in estimates, and they are also extremely cumbersome if not impractical to implement. We address this estimation problem by resorting to the technique of composite marginal likelihood (CML), an emerging inference approach in the statistics field that is based on the classical frequentist approach, is very simple to estimate, is easy to implement regardless of the number of count outcomes to be modeled jointly, and requires no simulation machinery whatsoever.The empirical analysis in the paper uses data drawn from the 2007 American Time Use Survey (ATUS) and provides important insights into the determinants of adults’ weekday activity episode generation behavior. The results underscore the substantial linkages in the activity episode generation of adults based on activity purpose and accompaniment type. The extent of this linkage varies by individual demographics, household demographics, day of the week, and season of the year. The results also highlight the flexibility of the CML approach to specify and estimate behaviorally rich structures to analyze inter-individual interactions in activity episode generation. 相似文献
24.
25.
This paper develops a structural and empirical model of subsistence activity behavior and income. Subsistence activity decisions (work participation and hours of work decisions) and income have an important bearing on activity and travel behavior of individuals. The proposed structural model represents an effort to analyze subsistence activity behavior and income earnings to support a better understanding, and reliable forecasting, of individual travel behavior. The empirical model formulates and estimates an integrated model of employment, hours of work and income which takes account of interdependencies among these choices and their structural relationships with other relevant variables. Social factors that inhibit an individual's employment and work hours decision and affect an individual's income are incorporated in the model. A sample of households from the Dutch National Mobility Panel is used in the empirical analysis. 相似文献
26.
Ipek N. Sener Rachel B. Copperman Ram M. Pendyala Chandra R. Bhat 《Transportation》2008,35(5):673-696
This paper presents a detailed analysis of discretionary leisure activity engagement by children. Children’s leisure activity
engagement is of much interest to transportation professionals from an activity-based travel demand modeling perspective,
to child development professionals from a sociological perspective, and to health professionals from an active lifestyle perspective
that can help prevent obesity and other medical ailments from an early age. Using data from the 2002 Child Development Supplement
of the Panel Study of Income Dynamics, this paper presents a detailed analysis of children’s discretionary activity engagement
by day of week (weekend versus weekday), location (in-home versus out-of-home), type of activity (physically active versus
passive), and nature of activity (structured versus unstructured). A mixed multiple discrete-continuous extreme value model
formulation is adopted to account for the fact that children may participate in multiple activities and allocate positive
time duration to each of the activities chosen. It is found that children participate at the highest rate and for the longest
duration in passive unstructured leisure activities inside the home. Children in households with parents who are employed,
higher income, or higher education were found to participate in structured outdoor activities at higher rates. The child activity
modeling framework and methodology presented in this paper lends itself for incorporation into larger activity-based travel
model systems where it is imperative that children’s activity-travel patterns be explicitly modeled—both from a child health
and well-being policy perspective and from a travel forecasting perspective.
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Rachel B. Copperman is currently a Ph.D. student at The University of Texas at Austin in transportation engineering. She received her M.S.E. from The University of Texas at Austin in Civil Engineering and her B.S. from the University of Virginia in Systems Engineering. Rachel grew up in Arlington, Virginia. Ram M. Pendyala is a Professor in Transportation at Arizona State University in Tempe. He teaches and conducts research in activity-based travel behavior modeling, multimodal transportation planning, and travel demand forecasting. He is the chair of the Transportation Research Board Committee on Traveler Behavior and Values and vice chair of the International Association for Travel Behaviour Research. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Ipek N. Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Rachel B. Copperman is currently a Ph.D. student at The University of Texas at Austin in transportation engineering. She received her M.S.E. from The University of Texas at Austin in Civil Engineering and her B.S. from the University of Virginia in Systems Engineering. Rachel grew up in Arlington, Virginia. Ram M. Pendyala is a Professor in Transportation at Arizona State University in Tempe. He teaches and conducts research in activity-based travel behavior modeling, multimodal transportation planning, and travel demand forecasting. He is the chair of the Transportation Research Board Committee on Traveler Behavior and Values and vice chair of the International Association for Travel Behaviour Research. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. 相似文献
27.
The current article proposes an approach to accommodate flexible spatial dependency structures in discrete choice models in
general, and in unordered multinomial choice models in particular. The approach is applied to examine teenagers’ participation
in social and recreational activity episodes, a subject of considerable interest in the transportation, sociology, psychology,
and adolescence development fields. The sample for the analysis is drawn from the 2000 San Francisco Bay Area Travel Survey
(BATS) as well as other supplementary data sources. The analysis considers the effects of a variety of built environment and
demographic variables on teenagers’ activity behavior. In addition, spatial dependence effects (due to common unobserved residential
neighborhood characteristics as well as diffusion/interaction effects) are accommodated. The variable effects indicate that
parents’ physical activity participation constitutes the most important factor influencing teenagers’ physical activity participation
levels, In addition, part-time student status, gender, and seasonal effects are also important determinants of teenagers’
social-recreational activity participation. The analysis also finds strong spatial correlation effects in teenagers’ activity
participation behaviors. 相似文献
28.
An analysis of the social context of children’s weekend discretionary activity participation 总被引:3,自引:1,他引:2
This paper examines the discretionary time-use of children, including the social context of children’s participations. Specifically,
the paper examines participation and time investment in in-home leisure as well as five different types of out-of-home discretionary
activities: (1) shopping, (2) social, (3) meals, (4) passive recreation (i.e., physically inactive recreation, such as going
to the movies or a concert), and (5) active recreation (i.e., physically active recreation, such as playing tennis or running).
The social context of children’s activity participation is also examined by focusing on the accompanying individuals in children’s
activity engagement. The accompanying arrangement is classified into one of six categories: (1) alone, (2) with mother and
no one else, (3) with father and no one else, (4) with both mother and father, and no one else, (5) with other individuals,
but no parents, and (6) with other individuals and one or both parents. The utility-theoretic Multiple Discrete-Continuous
Extreme Value (MDCEV) is employed to model time-use in one or more activity purpose–company type combinations. The data used
in the analysis is drawn from the 2002 Child Development Supplement (CDS) to the U.S. Panel Study Income Dynamics (PSID).
The results from the model can be used to examine the time-use choices of children, as well as to assess the potential impacts
of urban and societal policies on children’s activity participation and time-use decisions. Our findings also emphasize the
need to collect, in future travel surveys, more extensive and higher quality data capturing the intra- and inter-household
interactions between individuals (including children). To our knowledge, the research in this paper is the first transportation-related
study to rigorously and comprehensively analyze the social dimension of children’s activity participation.
Ipek Nese Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Dr. Chandra R. Bhat has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE). 相似文献
Chandra R. Bhat (Corresponding author)Email: |
Ipek Nese Sener is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. She received her M.S. degrees in Civil Engineering and in Architecture, and her B.S. degree in Civil Engineering from the Middle East Technical University in Ankara, Turkey. Dr. Chandra R. Bhat has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE). 相似文献
29.
Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends 总被引:1,自引:2,他引:1
This paper formulates a model for the allocation of total weekly discretionary time of individuals between in-home and out- of-home locations and between weekdays and the weekend. The model formulation takes the form of a continuous utility-maximizing resource allocation problem. The formulation is applied to an empirical analysis using data drawn from a 1985 time-use survey conducted in the Netherlands. This survey gathered time-use information from individuals over a period of one week and also collected detailed household-personal socio-demographic data. The empirical analysis uses household socio-demographics, individual socio-demographics, and work-related characteristics as the explanatory variables. Among the explanatory variables, age of the individual and work duration during the weekdays appear to be the most important determinants of discretionary time allocation. 相似文献
30.
Erika Spissu Abdul Rawoof Pinjari Chandra R. Bhat Ram M. Pendyala Kay W. Axhausen 《Transportation》2009,36(5):483-510
Activity-travel behavior research has hitherto focused on the modeling and understanding of daily time use and activity patterns
and resulting travel demand. In this particular paper, an analysis and modeling of weekly activity-travel behavior is presented
using a unique multi-week activity-travel behavior data set collected in and around Zurich, Switzerland. The paper focuses
on six categories of discretionary activity participation to understand the determinants of, and the inter-personal and intra-personal
variability in, weekly activity engagement at a detailed level. A panel version of the Mixed Multiple Discrete Continuous
Extreme Value model (MMDCEV) that explicitly accounts for the panel (or repeated-observations) nature of the multi-week activity-travel
behavior data set is developed and estimated on the data set. The model also controls for individual-level unobserved factors
that lead to correlations in activity engagement preferences across different activity types. To our knowledge, this is the
first formulation and application of a panel MMDCEV structure in the econometric literature. The analysis suggests the high
prevalence of intra-personal variability in discretionary activity engagement over a multi-week period along with inter-personal
variability that is typically considered in activity-travel modeling. In addition, the panel MMDCEV model helped identify
the observed socio-economic factors and unobserved individual specific factors that contribute to variability in multi-week
discretionary activity participation.
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at the University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Rawoof Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from the University of Texas at Austin. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use—transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Kay W. Axhausen is a Professor of Transport Planning at the Swiss Federal Institute of Technology (ETH) Zurich. Prior to his appointment at ETH, he worked at the Leopold Franzens University of Innsbruck, Imperial College London and the University of Oxford. He has been involved in the measurement and modelling of travel behaviour for the last 25 years, contributing especially to the literature on stated preferences, microsimulation of travel behaviour, valuation of travel time and its components, parking behaviour, activity scheduling and travel diary data collection. 相似文献
Kay W. AxhausenEmail: |
Erika Spissu is currently a Research Fellow at the University of Cagliari (Italy). She received her Ph.D. from the University of Palermo and University of Cagliari (Italy) in Transport techniques and economics. She spent the past 2 years at the University of Texas at Austin as a Research Scholar focusing primarily in activity-based travel behavior modeling, time use analysis, and travel demand forecasting. Abdul Rawoof Pinjari is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa. His research interests include time-use and travel-behavior analysis, and activity-based approaches to travel-demand forecasting. He has his Ph.D. from the University of Texas at Austin. Chandra R. Bhat is a Professor in Transportation at The University of Texas at Austin. He has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE), and the 2008 Wilbur S. Smith Distinguished Transportation Educator Award from the Institute of Transportation Engineers (ITE). He is the immediate past chair of the Transportation Research Board Committee on Transportation Demand Forecasting and the International Association for Travel Behaviour Research. Ram M. Pendyala is a Professor of Transportation Systems in the Department of Civil, Environmental, and Sustainable Engineering at Arizona State University. He teaches and conducts research in travel behavior analysis, travel demand modeling and forecasting, activity-based microsimulation approaches, and time use. He specializes in integrated land use—transport models, transport policy formulation, and public transit planning and design. He is currently the Vice-Chair of the International Association for Travel Behavior Research and is the immediate past chair of the Transportation Research Board Committee on Traveler Behavior and Values. He has his PhD from the University of California at Davis. Kay W. Axhausen is a Professor of Transport Planning at the Swiss Federal Institute of Technology (ETH) Zurich. Prior to his appointment at ETH, he worked at the Leopold Franzens University of Innsbruck, Imperial College London and the University of Oxford. He has been involved in the measurement and modelling of travel behaviour for the last 25 years, contributing especially to the literature on stated preferences, microsimulation of travel behaviour, valuation of travel time and its components, parking behaviour, activity scheduling and travel diary data collection. 相似文献