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1.
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
2.
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
3.
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
4.
This study explores the relationships between adoption and consideration of three travel-related strategy bundles (travel
maintaining/increasing, travel reducing, and major location/lifestyle change), linking them to a variety of explanatory variables.
The data for this study are the responses to a fourteen-page survey returned by nearly 1,300 commuting workers living in three
distinct San Francisco Bay area neighborhoods in May 1998. We first identified patterns of adoption and consideration among
the bundles, using pairwise correlation tests. The test results indicate that those who have adopted coping strategies continue
to seek for improvements across the spectrum of generalized cost, but perhaps most often repeating the consideration of a
previously-adopted bundle. Furthermore, we developed a multivariate probit model for individuals’ simultaneous consideration
of the three bundles. It is found that in addition to the previous adoption of the bundles, qualitative and quantitative Mobility-related
variables, Travel Attitudes, Personality, Lifestyle, Travel Liking, and Sociodemographics significantly affect individual
consideration of the strategy bundles. Overall, the results of this study give policy makers and planners insight into understanding
the dynamic nature of individuals’ responses to travel-related strategies, as well as differences between the responses to
congestion that are assumed by policy makers and those that are actually adopted by individuals.
Sangho Choo is a Research Associate at The Korea Transport Institute. His research interests include travel demand modeling, travel survey methods with GPS, and travel behavior modeling. Patricia L. Mokhtarian is a professor of Civil and Environmental Engineering, chair of the interdisciplinary Transportation Technology and Policy MS/PhD program, and Associate Director for Education of the Institute of Transportation Studies at the University of California, Davis. She has been modeling travel behavior and attitudes for more than 30 years. 相似文献
Patricia L. Mokhtarian (Corresponding author)Email: |
Sangho Choo is a Research Associate at The Korea Transport Institute. His research interests include travel demand modeling, travel survey methods with GPS, and travel behavior modeling. Patricia L. Mokhtarian is a professor of Civil and Environmental Engineering, chair of the interdisciplinary Transportation Technology and Policy MS/PhD program, and Associate Director for Education of the Institute of Transportation Studies at the University of California, Davis. She has been modeling travel behavior and attitudes for more than 30 years. 相似文献
5.
ABSTRACTThe study of social networks in activity-travel research has recently gained momentum because social activities and social influence were relatively poorly explained in activity-based models of travel demand. Over the last decade, many scholars have shown interest in identifying personal social networks that constitute an important source of explanation of activity-travel behaviour. This paper seeks to review two research streams: social networks and activity-travel behaviour, and social influence and travel decisions. We classify models, summarise empirical findings and discuss important issues that require further research. 相似文献
6.
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
7.
Lawrence Frank Mark Bradley Sarah Kavage James Chapman T. Keith Lawton 《Transportation》2008,35(1):37-54
The primary purpose of this study was to investigate how relative associations between travel time, costs, and land use patterns
where people live and work impact modal choice and trip chaining patterns in the Central Puget Sound (Seattle) region. By
using a tour-based modeling framework and highly detailed land use and travel data, this study attempts to add detail on the
specific land use changes necessary to address different types of travel, and to develop a comparative framework by which
the relative impact of travel time and urban form changes can be assessed. A discrete choice modeling framework adjusted for
demographic factors and assessed the relative effect of travel time, costs, and urban form on mode choice and trip chaining
characteristics for the three tour types. The tour based modeling approach increased the ability to understand the relative
contribution of urban form, time, and costs in explaining mode choice and tour complexity for home and work related travel.
Urban form at residential and employment locations, and travel time and cost were significant predictors of travel choice.
Travel time was the strongest predictor of mode choice while urban form the strongest predictor of the number of stops within
a tour. Results show that reductions in highway travel time are associated with less transit use and walking. Land use patterns
where respondents work predicted mode choice for mid day and journey to work travel.
Lawrence Frank is an Associate Professor and Bombardier Chair in Sustainable Transportation at the University of British Columbia and a Senior Non-Resident Fellow of the Brookings Institution and Principal of Lawrence Frank and Company. He has a PhD in Urban Design and Planning from the University of Washington. Mark Bradley is Principal, Mark Bradley Research & Consulting, Santa Barbara California. He has a Master of Science in Systems Simulation and Policy Design from the Dartmouth School of Engineering and designs forecasting and simulation models for assessment of market-based policies and strategies. Sarah Kavage is a Senior Transportation Planner and Special Projects Manager at Lawrence Frank and Company. She has a Masters in Urban Design and Planning from the University of Washington and is a writer and an artist based in Seattle. James Chapman is a Principal Transportation Planner and Analyst at Lawrence Frank and Company in Atlanta Georgia. He has a Masters in Engineering from the Georgia Institute of Technology. T. Keith Lawton transport modeling consultant and past Director of Technical services, Metro Planning Department, Portland, OR, has been active in model development for over 40 years. He has a BSc. in Civil Engineering from the University of Natal (South Africa), and an M.S. in Civil and Environmental Engineering from Duke University. He is a member and past Chair of the TRB Committee on Passenger Travel Demand Forecasting. 相似文献
T. Keith LawtonEmail: |
Lawrence Frank is an Associate Professor and Bombardier Chair in Sustainable Transportation at the University of British Columbia and a Senior Non-Resident Fellow of the Brookings Institution and Principal of Lawrence Frank and Company. He has a PhD in Urban Design and Planning from the University of Washington. Mark Bradley is Principal, Mark Bradley Research & Consulting, Santa Barbara California. He has a Master of Science in Systems Simulation and Policy Design from the Dartmouth School of Engineering and designs forecasting and simulation models for assessment of market-based policies and strategies. Sarah Kavage is a Senior Transportation Planner and Special Projects Manager at Lawrence Frank and Company. She has a Masters in Urban Design and Planning from the University of Washington and is a writer and an artist based in Seattle. James Chapman is a Principal Transportation Planner and Analyst at Lawrence Frank and Company in Atlanta Georgia. He has a Masters in Engineering from the Georgia Institute of Technology. T. Keith Lawton transport modeling consultant and past Director of Technical services, Metro Planning Department, Portland, OR, has been active in model development for over 40 years. He has a BSc. in Civil Engineering from the University of Natal (South Africa), and an M.S. in Civil and Environmental Engineering from Duke University. He is a member and past Chair of the TRB Committee on Passenger Travel Demand Forecasting. 相似文献
8.
Khandker M. Nurul Habib 《Transportation》2011,38(1):123-151
The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization
(RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure
choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history
of activity participation as well as changes in time budget availability over the day. For empirical application, the model
is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies
activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type
and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high
degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time
of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements
on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical
model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer
duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements,
but longer travel time requirements typically lead to longer-duration social activities. 相似文献
9.
Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach 总被引:5,自引:2,他引:3
Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents
in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies
confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying
causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options
affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence
of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study
took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential
preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional
neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate
the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results
provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative
transportation options will actually lead to less driving and more walking.
Xinyu (Jason) Cao is a research fellow in the Upper Great Plains Transportation Institute at North Dakota State University. His research interests include the influences of land use on travel and physical activity, and transportation planning. Patricia L. Mokhtarian is a professor of Civil and Environmental Engineering, Chair of the interdisciplinary Transportation Technology and Policy graduate program, and Associate Director for Education of the Institute of Transportation Studies at the University of California, Davis. She specializes in the study of travel behavior. Susan L. Handy is a professor in the Department of Environmental Science and Policy and Director of the Sustainable Transportation Center at the University of California, Davis. Her research interests center around the relationships between transportation and land use, particularly the impact of neighborhood design on travel behavior. 相似文献
Susan L. HandyEmail: |
Xinyu (Jason) Cao is a research fellow in the Upper Great Plains Transportation Institute at North Dakota State University. His research interests include the influences of land use on travel and physical activity, and transportation planning. Patricia L. Mokhtarian is a professor of Civil and Environmental Engineering, Chair of the interdisciplinary Transportation Technology and Policy graduate program, and Associate Director for Education of the Institute of Transportation Studies at the University of California, Davis. She specializes in the study of travel behavior. Susan L. Handy is a professor in the Department of Environmental Science and Policy and Director of the Sustainable Transportation Center at the University of California, Davis. Her research interests center around the relationships between transportation and land use, particularly the impact of neighborhood design on travel behavior. 相似文献
10.
Transportation specialists, urban planners, and public health officials have been steadfast in encouraging active modes of
transportation over the past decades. Conventional thinking, however, suggests that providing infrastructure for cycling and
walking in the form of off-street trails is critically important. An outstanding question in the literature is how one’s travel
is affected by the use of such facilities and specifically, the role of distance to the trail in using such facilities. This
research describes a highly detailed analysis of use along an off-street facility in Minneapolis, Minnesota, USA. The core
questions addressed in this investigation aim to understand relationships between: (1) the propensity of using the trail based
on distance from the trip origin and destination, and (2) how far out of their way trail users travel for the benefit of using
the trail and explanatory factors for doing so. The data used in the analysis for this research was collected as a human intercept
survey along a section of an off-street facility. The analysis demonstrates that a cogent distance decay pattern exists and
that the decay function varies by trip purpose. Furthermore, we find that bicyclists travel, on average, 67% longer in order
to include the trail facility on their route. The paper concludes by explaining how the distance decay and shortest path versus
taken path analysis can aid in the planning and analysis of new trail systems.
Kevin J. Krizek is an Associate Professor of Planning and Design at the University of Colorado where he directs the Active Communities/Transportation Research Group. His research interests include land use-transportation policies and programs that influence household residential location decisions and travel behavior. He has published in the areas of transportation demand management, travel behavior, neighborhood accessibility, and sustainable development. He earned a Ph.D. in Urban Design and Planning and M.S.C.E. from the University of Washington in Seattle. His master’s degree in planning is from the University of North Carolina at Chapel Hill and his undergraduate degree is from Northwestern University. Ahmed El-Geneidy is a Post-Doctoral research fellow at the Department of Civil Engineering, University of Minnesota and Humphrey Institute of Public Affairs. El-Geneidy’s research interests include transit operations, travel behavior, land use and transportation planning, and accessibility/mobility measures in urban areas. He earned B.S. and M.S. degrees from the Department of Architectural Engineering at the University of Alexandria, Egypt, and continued his academic work at Portland State University, where he received a Graduate GIS Certificate and earned a Ph.D. in Urban Studies from Nohad A. Toulan School of Urban Studies and Planning. Kristin Thompson was a research assistant with ACT and currently works for Metro Transit in Minneapolis, Minnesota. 相似文献
Ahmed El-GeneidyEmail: |
Kevin J. Krizek is an Associate Professor of Planning and Design at the University of Colorado where he directs the Active Communities/Transportation Research Group. His research interests include land use-transportation policies and programs that influence household residential location decisions and travel behavior. He has published in the areas of transportation demand management, travel behavior, neighborhood accessibility, and sustainable development. He earned a Ph.D. in Urban Design and Planning and M.S.C.E. from the University of Washington in Seattle. His master’s degree in planning is from the University of North Carolina at Chapel Hill and his undergraduate degree is from Northwestern University. Ahmed El-Geneidy is a Post-Doctoral research fellow at the Department of Civil Engineering, University of Minnesota and Humphrey Institute of Public Affairs. El-Geneidy’s research interests include transit operations, travel behavior, land use and transportation planning, and accessibility/mobility measures in urban areas. He earned B.S. and M.S. degrees from the Department of Architectural Engineering at the University of Alexandria, Egypt, and continued his academic work at Portland State University, where he received a Graduate GIS Certificate and earned a Ph.D. in Urban Studies from Nohad A. Toulan School of Urban Studies and Planning. Kristin Thompson was a research assistant with ACT and currently works for Metro Transit in Minneapolis, Minnesota. 相似文献
11.
Jeremy Hackney Fabrice Marchal 《Transportation Research Part A: Policy and Practice》2011,45(4):296-309
Choice set formation, location and mode preferences, coordinated scheduling, alternative utility valuations, and shared mobility resources are among the many activity-travel issues hypothesized to be significantly influenced by traveler interdependencies. Empirical evidence lags theory, particularly about the geography of social networks. A simulation tool is presented to let the experimenter construct and test hypothetical interdependencies between geography, socially-linked travelers, and activity-travel choices. The exploratory tool is integrated in the Multi-Agent Transportation Simulation Toolbox (MatSim-T). Initially, any social network can be constructed and embedded in geography. It can remain static, or be adapted to the travel patterns of the agents. The interactions and exchanges between agents influencing socializing and/or travel behavior can be defined in substance and in time/space. The reward for socializing or being socially linked can be varied. Finally, the co-dependence of social factors and travel behavior can be studied. This paper introduces the model and presents verification results which illustrate the coupling of extremely simplified socializing assumptions and travel behavior. 相似文献
12.
Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations. 相似文献
13.
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.
This paper suggests using a proportional hazard model to predict personal income, for the purpose of imputing missing income
data in household travel surveys. The model has a hazard function that comprises two multiplicative components: (1) a non-parametric
baseline hazard function that is dependent only on the income level and (2) a function that is dependent only on the other
personal attributes of the survey respondents (excluding income). To estimate and validate the model, data is drawn from a
travel characteristics survey conducted in Hong Kong in year 2001. The model is found to have a much higher accuracy when
compared with a conventional ordered probit model based on the assumption that the logarithm of income is normally distributed.
C.·O. Tong is an Associate Professor at the Department of Civil Engineering, The University of Hong Kong. He received his B.Sc. (Eng.) degree from the University of Hong Kong, M.Sc. (Transportation Engineering) degree from Leeds University and Ph.D. degree from Monash University. His research interests are in transport demand modeling and dynamic network modeling. Jackie K. L. Lee worked as a Research Assistant at the Department of Civil Engineering, The University of Hong Kong during the period from March 2004 to April 2005. She received her B.Eng. and M.Eng. degrees in Civil Engineering from the Hong Kong Polytechnic University. She is a Chartered Engineer and is also Corporate Members of the Hong Kong Institution of Engineers and the Institution of Structural Engineers. 相似文献
C. O. TongEmail: |
C.·O. Tong is an Associate Professor at the Department of Civil Engineering, The University of Hong Kong. He received his B.Sc. (Eng.) degree from the University of Hong Kong, M.Sc. (Transportation Engineering) degree from Leeds University and Ph.D. degree from Monash University. His research interests are in transport demand modeling and dynamic network modeling. Jackie K. L. Lee worked as a Research Assistant at the Department of Civil Engineering, The University of Hong Kong during the period from March 2004 to April 2005. She received her B.Eng. and M.Eng. degrees in Civil Engineering from the Hong Kong Polytechnic University. She is a Chartered Engineer and is also Corporate Members of the Hong Kong Institution of Engineers and the Institution of Structural Engineers. 相似文献
15.
Social equity is increasingly becoming an important objective in transport planning and project evaluation. This paper provides a framework and an empirical investigation in the Greater Toronto and Hamilton Area (GTHA) examining the links between public transit accessibility and the risks of social exclusion, simply understood as the suppressed ability to conduct daily activities at normal levels. Specifically, we use a large-sample travel survey to present a new transport-geography concept termed participation deserts, neighbourhood-level clusters of lower than expected activity participation. We then use multivariate models to estimate where, and for whom, improvements in transit accessibility will effectively increase activity participation and reduce risks of transport-related social exclusion. Our results show that neighbourhoods with high concentrations of low-income and zero-car households located outside of major transit corridors are the most sensitive to having improvements in accessibility increase daily activity participation rates. We contend that transit investments providing better connections to these neighbourhoods would have the greatest benefit in terms of alleviating existing inequalities and reducing the risks of social exclusion. The ability for transport investments to liberate suppressed activity participation is not currently being predicted or valued in existing transport evaluation methodologies, but there is great potential in doing so in order to capture the social equity benefits associated with increasing transit accessibility. 相似文献
16.
This paper presents a multiple discrete-continuous econometric structure to model the daily time-investment decisions of couples
in solo- and joint-discretionary activities incorporating intra-personal and inter-personal inter-dependencies. The empirical
model was estimated using data from the 2000 Bay Area Travel Survey. The results indicate evidence of the positive impact
of vehicle availability on independent activity participation and the negative impacts of the presence of children and mandatory
time investments on the joint discretionary-activity engagement of the spouses. In addition, we also find the mandatory- and
maintenance-activity-participation characteristics of the spouse to influence the discretionary activity choices of individuals.
Finally, the analysis also indicates a strong impact of common unobserved factors on the decisions of couples. From a policy
analysis perspective, these results imply that demand-management actions directly impacting one adult could also result in
changes to the activity patterns of his/her spouse and to changes in joint activity participation characteristics.
Dr. Sivaramakrishnan Srinivasan is an Assistant Professor in the Department of Civil and Coastal Engineering at the University of Florida. His research interests
include travel-behavior analysis, activity-based travel-demand modeling, and the application of advanced econometric methods
for transportation problems.
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
17.
Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand 总被引:1,自引:0,他引:1
Diana Kusumastuti Els Hannes Davy Janssens Geert Wets Benedict G. C. Dellaert 《Transportation》2010,37(4):647-661
Activity-based models for modeling individuals’ travel demand have come to a new era in addressing individuals’ and households’
travel behavior on a disaggregate level. Quantitative data are mainly used in this domain to enable a realistic representation
of individual choices and a true assessment of the impact of different Travel Demand Management measures. However, qualitative
approaches in data collection are believed to be able to capture aspects of individuals’ travel behavior that cannot be obtained
using quantitative studies, such as detailed decision making process information. Therefore, qualitative methods may deepen
the insight into human’s travel behavior from an agent-based perspective. This paper reports on the application of a qualitative
semi-structured interview method, namely the Causal Network Elicitation Technique (CNET), for eliciting individuals’ thoughts
regarding fun-shopping related travel decisions, i.e. timing, shopping location and transport mode choices. The CNET protocol
encourages participants to think aloud about their considerations when making decisions. These different elicited aspects
are linked with causal relationships and thus, individuals’ mental representations of the task at hand are recorded. This
protocol is tested in the city centre of Hasselt in Belgium, using 26 young adults as respondents. Response data are used
to apply the Association Rules, a fairly common technique in machine learning. Results highlight different interrelated contexts,
instruments and values considered when planning a trip. These findings can give feedback to current AB models to raise their
behavioral realism and to improve modeling accuracy. 相似文献
18.
Bilal Farooq Eric J. Miller 《Transportation Research Part A: Policy and Practice》2012,46(7):1030-1053
Investigating the factors and processes that influence the spatiotemporal distribution of built space and population in an urban area, plays an extremely important role in our greater understanding of the urban travel behaviour. Existing location of activity centres, especially home and work, strongly influences the short-term individual-level decisions such as mode of transportation, and long-term household-level decisions such as change in job and residential location. Conditions in the built space market also affect households’ and firms’ location and relocation decisions, and hence influence the general travel patterns in an urban area. In this context, this paper addresses a very important, but at the same time, not very widely investigated dimension that plays a key role in the evolution of built space and population distribution: Market. A disequilibrium based microsimulation modelling framework is developed for the built space markets. This framework is then used to operationalize the Greater Toronto and Hamilton Area’s owner-occupied housing market within Integrated Land Use Transportation and Environment (ILUTE) modelling system. Simulation results captured heterogeneity in the transaction prices, due to type of dwellings and different market conditions, in a very disaggregate fashion. The proposed methodology is validated by running the simulation from 1986 to 2006 and comparing the results with the historic data. 相似文献
19.
What neighborhood are you in? Empirical findings of relationships between household travel and neighborhood characteristics 总被引:1,自引:0,他引:1
In recent years, there have been studies of the influence of neighborhood or built environment characteristics on residential
location choice and household travel behavior. Interestingly, there is no uniform definition of neighborhood in the literature
and the definition is often vague. This paper presents an alternative way of defining neighborhood and neighborhood type,
which involves innovative usage of public data sources. Furthermore, the paper investigates the interaction between neighborhood
environment and household travel in the US. A neighborhood here is spatially identical to a census tract. A neighborhood type
identifies a group of neighborhoods with similar neighborhood socio-economic, demographic, and land use characteristics. This
is accomplished by performing log-likelihood clustering on the Census Transportation Planning Package (CTPP) 2000 data. Five
household travel measures, i.e., number of trips per household, mode share, average travel distance and time per trip, and
vehicle miles of travel (VMT), are then compared across the resulting 10 neighborhood types, using the 2001 National Household
Travel Survey (NHTS) household and trip files. It is found that household life cycle status and residential location are not
independent. Transit availability at place of residence tends to increase the transit mode share regardless of household automobile
ownership and income level, and job-housing trade-offs are evident when mobility is not of concern. The study also reveals
racial preference in residential location and contrasting travel characteristics among ethnic groups.
Dr. Jie Lin (Jane) is an assistant professor in Department of Civil and Materials Engineering and a researcher with the Institute for Environmental Science and Policy at University of Illinois at Chicago. Her research is focused on transportation demand analysis, data mining, and transportation sustainability in private, freight, and public transportation systems. Dr. Liang Long received a Doctorate degree in Civil Engineering from the University of Illinois at Chicago and a Master’s degree in Civil Engineering (Transportation Engineering) from Tongji University. She is currently with Cambridge Systematics as a transportation modeler with expertise in travel demand forecasting, geographic information systems (GIS) and market research. 相似文献
Liang LongEmail: |
Dr. Jie Lin (Jane) is an assistant professor in Department of Civil and Materials Engineering and a researcher with the Institute for Environmental Science and Policy at University of Illinois at Chicago. Her research is focused on transportation demand analysis, data mining, and transportation sustainability in private, freight, and public transportation systems. Dr. Liang Long received a Doctorate degree in Civil Engineering from the University of Illinois at Chicago and a Master’s degree in Civil Engineering (Transportation Engineering) from Tongji University. She is currently with Cambridge Systematics as a transportation modeler with expertise in travel demand forecasting, geographic information systems (GIS) and market research. 相似文献
20.
Various models that incorporate concepts from Non-Cooperative Game Theory (NCGT) are described in the transport literature.
Game Theory provides powerful tools for analysing transport systems, but these tools have some drawbacks that should be recognised.
In the current paper we review games that describe transport problems and discuss them within a uniform context. Although
the paper does not introduce new tools, it presents insights concerning the relations between transport models and games.
We divide existing games into groups and show that some common features characterise multiple games. We distinguish between
games that make a conceptual contribution and games that are suitable for application. Compact or symmetric game structures
make remarkable observations but often do not support actual decision-making. Less aesthetic formats, most of which are Stackelberg
games between authorities and travellers, are stronger as instruments that assist in determining real-life policies; these
formulations can be treated by practitioners as mathematical programs with equilibrium constraints and not as games.
Yaron Hollander is currently conducting economic research of bus reliability at the Institute for Transport Studies at the University of
Leeds. He previously worked for the Technion—Israel Institute of Technology; for the Israel Institute for Transportation Planning
and Research; and for the public transport department at Ayalon Highways Co.
Joseph N. Prashker is a professor at the Faculty of Civil and Environmental Engineering at the Technion—Israel Institute of Technology. Till
recently he served as head of the Transportation Research Institute at the Technion. His interests are behavioural demand
models, network analysis, and Game Theory applications in transportation. 相似文献