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11.
Chandra R. Bhat 《Transportation Research Part B: Methodological》1996,30(6):465-480
The model developed in this paper generalizes (in the context of multiple exit states from a duration spell) extant competing risk methods which tie the exit state of duration very tightly with the length of duration. In the current formulation, the exit state is modeled explicitly and jointly with duration models for each potential exit state. The model developed here, however, is much broader in its applicability than only to the competing risk situation; it is applicable to multiple durations arising from multiple entrance states, multiple exit states, or a combination of entrance and exit states. Multiple entrance states occur frequently in many situations, but have received little attention in the literature. Explicit consideration of the entrance state is important, even in single or multiple competing risk models in order to accommodate the sample selection in duration based on the no-entry/entry (to the duration spell) outcome. The generalized multiple durations model developed in the paper is applied to an empirical analysis of activity behavior during the return home from work. 相似文献
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In the US, the rise in motorized vehicle travel has contributed to serious societal, environmental, economic, and public health
problems. These problems have increased the interest in encouraging non-motorized modes of travel (walking and bicycling).
The current study contributes toward this objective by identifying and evaluating the importance of attributes influencing
bicyclists’ route choice preferences. Specifically, the paper examines a comprehensive set of attributes that influence bicycle
route choice, including: (1) bicyclists’ characteristics, (2) on-street parking, (3) bicycle facility type and amenities,
(4) roadway physical characteristics, (5) roadway functional characteristics, and (6) roadway operational characteristics.
The data used in the analysis is drawn from a web-based stated preference survey of Texas bicyclists. The results of the study
emphasize the importance of a comprehensive evaluation of both route-related attributes and bicyclists’ demographics in bicycle
route choice decisions. The empirical results indicate that travel time (for commuters) and motorized traffic volume are the
most important attributes in bicycle route choice. Other route attributes with a high impact include number of stop signs,
red light, and cross-streets, speed limits, on-street parking characteristics, and whether there exists a continuous bicycle
facility on the route.
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. Naveen Eluru is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. He received his M.S. degree in Civil Engineering from The University of Texas at Austin, and his Bachelors in Technology Degree from Indian Institute of Technology in Madras, India. 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. Naveen Eluru is currently a Ph.D. candidate in transportation engineering at The University of Texas at Austin. He received his M.S. degree in Civil Engineering from The University of Texas at Austin, and his Bachelors in Technology Degree from Indian Institute of Technology in Madras, India. 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
14.
Erika Spissu Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat 《Transportation》2009,36(4):403-422
In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles
drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete–continuous model system formulated in this study
explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e.,
self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive
distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components.
The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation
approaches for joint discrete–continuous model systems. The model system, when applied to simulate the impacts of a doubling
in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.
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 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. 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. 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: |
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 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. 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. 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
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This study presents a unified framework to understand the weekday recreational activity participation time-use of adults,
with an emphasis on the time expended in physically active recreation pursuits by location and by time-of-day. Such an analysis
is important for a better understanding of how individuals incorporate physical activity into their daily activities on a
typical weekday, and can inform the development of effective policy interventions to facilitate physical activity. Furthermore,
such a study of participation and time use in recreational activity episodes contributes to activity-based travel demand modeling,
since recreational activity participation comprises a substantial share of individuals’ total non-work activity participation.
The methodology employed here is the multiple discrete continuous extreme value (MDCEV) model, which provides a unified framework
to explicitly and endogenously examine time use by type, location, and timing. The data for the empirical analysis is drawn
from the 2000 Bay Area Travel Survey (BATS), supplemented with other secondary sources that provide information on physical
environment variables. To our knowledge, this is the first study to jointly address the issues of ‘where’, ‘when’ and ‘how
much’ individuals choose to participate in ‘what type of (recreational) activity’. 相似文献
16.
Stacey G. Bricka Sudeshna Sen Rajesh Paleti Chandra R. Bhat 《Transportation Research Part C: Emerging Technologies》2012,21(1):67-88
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 survey-reported 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 GPS-recorded) 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-h 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. 相似文献
17.
Nazneen Ferdous Abdul Rawoof Pinjari Chandra R. Bhat Ram M. Pendyala 《Transportation》2010,37(3):363-390
This paper proposes a multiple discrete continuous nested extreme value (MDCNEV) model to analyze household expenditures for
transportation-related items in relation to a host of other consumption categories. The model system presented in this paper
is capable of providing a comprehensive assessment of how household consumption patterns (including savings) would be impacted
by increases in fuel prices or any other household expense. The MDCNEV model presented in this paper is estimated on disaggregate
consumption data from the 2002 Consumer Expenditure Survey data of the United States. Model estimation results show that a
host of household and personal socio-economic, demographic, and location variables affect the proportion of monetary resources
that households allocate to various consumption categories. Sensitivity analysis conducted using the model demonstrates the
applicability of the model for quantifying consumption adjustment patterns in response to rising fuel prices. It is found
that households adjust their food consumption, vehicular purchases, and savings rates in the short run. In the long term,
adjustments are also made to housing choices (expenses), calling for the need to ensure that fuel price effects are adequately
reflected in integrated microsimulation models of land use and travel. 相似文献
18.
Felipe F. Dias Patrícia S. Lavieri Venu M. Garikapati Sebastian Astroza Ram M. Pendyala Chandra R. Bhat 《Transportation》2017,44(6):1307-1323
There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014–2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services. 相似文献
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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. 相似文献
20.
Smrutiranjan Mohapatra 《船舶与海洋工程学报》2016,15(2):112-122
The interaction of oblique incident water waves with a small bottom deformation on a porous ocean-bed is examined analytically here within the framework of linear water wave theory. The upper surface of the ocean is assumed to be covered by an infinitely extended thin uniform elastic plate, while the lower surface is bounded by a porous bottom surface having a small deformation. By employing a simplified perturbation analysis, involving a small parameter δ(=1), which measures the smallness of the deformation, the governing Boundary Value Problem (BVP) is reduced to a simpler BVP for the first-order correction of the potential function. This BVP is solved using a method based on Green’s integral theorem with the introduction of suitable Green’s function to obtain the first-order potential, and this potential function is then utilized to calculate the first-order reflection and transmission coefficients in terms of integrals involving the shape function c(x) representing the bottom deformation. Consideration of a patch of sinusoidal ripples shows that when the quotient of twice the component of the incident field wave number propagating just below the elastic plate and the ripple wave number approaches one, the theory predicts a resonant interaction between the bed and the surface below the elastic plate. Again, for small angles of incidence, the reflected wave energy is more as compared to the other angles of incidence. It is also observed that the reflected wave energy is somewhat sensitive to the changes in the flexural rigidity of the elastic plate, the porosity of the bed and the ripple wave numbers. The main advantage of the present study is that the results for the values of reflection and transmission coefficients obtained are found to satisfy the energy-balance relation almost accurately. 相似文献