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1.
The timing and duration of an activity episode are two important temporal aspects of activity-travel behavior. Understanding the causal relationship between these two variables would be useful in the development of activity-based travel demand modeling systems. This paper investigates the relationship between these two variables by considering two different causal structures – one structure in which time-of-day choice is determined first and influences duration and a second structure in which activity duration is determined first and affects time-of-day choice. These two structures are estimated within a discrete-continuous simultaneous equations framework employing a full-information maximum likelihood methodology that allows error covariance. The estimation is performed separately for commuter and non-commuter samples drawn from a 1996 household travel survey data set from the Tampa Bay area in Florida. The results of the model estimation effort show that the causal structure in which activity duration precedes or affects activity timing (time of day choice) performs better for the non-commuter sample. For the commuter sample, the findings were less conclusive with both causal structures offering equally good statistical measures of fit. In addition, for the commuter sample, all error correlations were found to be zero. These two findings suggest that time of day choice and activity episode duration are only loosely related for the commuter sample, possibly due to the relatively non-discretionary and inflexible work activity and travel. 相似文献
2.
Gopindra Sivakumar Nair Sebastian Astroza Chandra R. Bhat Sara Khoeini Ram M. Pendyala 《Transportation》2018,45(6):1623-1637
Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances. 相似文献
3.
Maren L. Outwater Greg Spitz John Lobb Margaret Campbell Bhargava Sana Ram Pendyala William Woodford 《Transportation》2011,38(4):605-623
This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical
solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The
principal findings were that the representation of awareness of transit services is significantly different than the underlying
assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore,
inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional
methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper
documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis
of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing
these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte)
with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a
need, and a demonstration of the research in practice for at least one case study city. 相似文献
4.
This paper studies changes in the relationship between household car ownership and income by household type. Ordered response probit models of car ownership are estimated for a sample of households repeatedly at six time points to track the evolution of income elasticities of car ownership over time. Elasticities of car ownership are found to change over time, questioning the existence of a unique equilibrium point between demand and supply that is implicitly assumed in traditional cross-sectional discrete choice car ownership models. Moreover, different household types and households that underwent household type transitions showed differing patterns of change in elasticities. Observed trends in car ownership and income clearly show behavioral asymmetry where the elasticity of procuring an additional car is greater than that of disposing a car. This too shows the inadequacy of traditional cross-sectional models of car ownership which tend to predict symmetry in behavior. The study suggests the importance of incorporating dynamic trends into the forecasting process, which can be accomplished through the use of longitudinal data. 相似文献
5.
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
6.
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
7.
A spatial and temporal analysis of travel diary data collected during the State of California Telecommuting Pilot Project is performed to determine the impacts of telecommuting on household travel behavior. The analysis is based on geocoded trip data where missing trips and trip attributes have been augmented to the extent possible. The results confirm the earlier finding that the Pilot Project telecommuters substantially reduced travel; on telecommuting days, the telecommuters made virtually no commute trips, reduced peak-period trips by 60%, total distance traveled by 75%, and freeway miles by 90%. The spatial analysis of the trip records has shown that the telecommuters chose non-work destinations that are closer to home; they exhibited contracted action spaces after the introduction of telecommuting. Importantly, this contraction took place on both telecommuting days and commuting days. The telecommuters distributed their trips, over the day and avoided peak-period travel on telecommuting days. Non-work trips, however, show similar patterns of temporal distribution on telecommuting days and commuting days. Non-work trips continued to be made during the lunch period and late afternoon and evening hours. 相似文献
8.
Modeling residential sorting effects to understand the impact of the built environment on commute mode choice 总被引:5,自引:2,他引:3
Abdul Rawoof Pinjari Ram M. Pendyala Chandra R. Bhat Paul A. Waddell 《Transportation》2007,34(5):557-573
This paper presents an examination of the significance of residential sorting or self selection effects in understanding the
impacts of the built environment on travel choices. Land use and transportation system attributes are often treated as exogenous
variables in models of travel behavior. Such models ignore the potential self selection processes that may be at play wherein
households and individuals choose to locate in areas or built environments that are consistent with their lifestyle and transportation
preferences, attitudes, and values. In this paper, a simultaneous model of residential location choice and commute mode choice
that accounts for both observed and unobserved taste variations that may contribute to residential self selection is estimated
on a survey sample extracted from the 2000 San Francisco Bay Area household travel survey. Model results show that both observed
and unobserved residential self selection effects do exist; however, even after accounting for these effects, it is found
that built environment attributes can indeed significantly impact commute mode choice behavior. The paper concludes with a
discussion of the implications of the model findings for policy planning.
相似文献
Paul A. WaddellEmail: |
9.
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
10.
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