Browsing by Subject "unobserved heterogeneity"

Now showing 1 - 10 of 10

An analysis of evening commute stop-making behavior using repeated choice observations from a multi-day survey
(Elsevier, 1999) Bhat, Chandra R.
This paper examines the number of stops made by individuals during their evening commute. The paper applies a methodological framework that relates stop-making to relevant individual, land-use, and work-related characteristics. The framework also accommodates unobserved variation in stop-making propensity across individuals in intrinsic preferences and in responsiveness to work-related attributes. The empirical analysis uses a sample of repeated choice observations from a multi-day sample of workers drawn from the 1990 San Francisco Bay area household survey. The results indicate that the proposed model provides a superior data fit relative to a model that ignores unobserved variations in stop-making propensity across individuals. The model in this paper also provides important behavioral insights which are masked by the model that disregards unobserved variations.
An analysis of the impact of information and communication technologies on non-maintenance shopping activities
(Elsevier, 2003) Bhat, Chandra R.; Sivakumar, Aruna; Axhausen, Kay W.
This paper examines the use and travel impacts of two forms of Information and Communication Technologies (ICTs): mobile telephones and computers. The travel impacts are examined in the context of participation in out-of-home non-maintenance shopping activities over a multiweek period through the modeling of the duration between successive shopping activity participations. The empirical analysis uses a continuous six-week travel survey collected in the cities of Halle and Karlsruhe in Germany in the Fall of 1999. The results indicate that the effects of ICTs on activity-travel patterns are mediated by individual sociodemographic and locational factors, as well as by unobserved individual characteristics. The results also show that the substitution between mobile phone use and shopping travel is grossly underestimated if the effects of common unobserved factors affecting mobile phone use and shopping travel are not considered. In addition, there is quite substantial intra-individual variation in intershopping duration.
A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity
(Elsevier, 1996) Bhat, Chandra R.
Activity duration is an important component of the activity participation behavior of individuals, and therefore, an important determinant of individual travel behavior. In this paper, we examine the factors affecting shopping activity duration during the return home from work and develop a comprehensive methodological framework to estimate a stochastic hazard-based duration model from grouped (interval-level) failure data. The framework accommodates a nonparametric baseline hazard distribution and allows for nonparametric control of unobserved heterogeneity, while incorporating the effects of covariates. The framework also facilitates statistical testing of alternative parametric assumptions on the baseline hazard distribution and on the unobserved heterogeneity distribution. Our empirical results indicate significant effects of unobserved heterogeneity on shopping activity duration of individuals. Further, we find that parametric forms for the baseline hazard and unobserved heterogeneity distributions are inadequate, and are likely to lead to substantial biases in covariate effects and hazard dynamics. The empirical results also provide insights into the determinants of shopping activity duration during the commute trip.
Incorporating observed and unobserved heterogeneity in urban work travel mode choice modeling
(Institute for Operations Research and the Management Sciences, 2000) Bhat, Chandra R.
An individual's intrinsic mode preference and responsiveness to level-of-service variables affects her or his travel mode choice for a trip. The mode preference and responsiveness will, in general, vary across individuals based on observed (to an analyst) and unobserved (to an analyst) individual characteristics. The current paper formulates a multinomial-logit based model of travel mode choice that accommodates variations in mode preferences and responsiveness to level-of-service due to both observed and unobserved individual characteristics. The model parameters are estimated using a maximum simulated log- likelihood approach. The model is applied to examine urban work travel mode choice in a multiday sample of workers from the San Francisco Bay area.
Intershopping Duration: An Analysis Using Multiweek Data
(Elsevier, 2004) Bhat, Chandra R.; Frusti, Teresa; Zhao, Huimin; Schonfelder, Stefan; Axhausen Kay W.
This study examines the rhythms in the shopping activity participation of individuals over a multiweek period by modeling the duration between successive shopping participations. A hazard based duration model is used to model intershopping duration, and a latent segmentation method is applied to distinguish between erratic shoppers and regular shoppers. The paper applies the methodology to examine the regularity and frequency of shopping behavior of individuals using a continuous six-week travel survey collected in the cities of Halle and Karlsruhe in Germany in the fall of 1999. The empirical results underscore the need to adopt a flexible hazard model form for analyzing intershopping durations. The results also provide important insights into the determinants of the regularity and frequency of individuals' shopping activity participation behavior.
Joint Model of Choice of Residential Neighborhood and Bicycle Ownership: Accounting for Self-Selection and Unobserved Heterogeneity
(National Academy of Sciences, 2008) Pinjari, Abdul R.; Eluru, Naveen; Bhat, Chandra R.; Pendyala, Ram M.; Spissu, Erika
This paper presents a joint model of residential neighborhood type choice and bicycle ownership. The objective is to isolate the true causal effects of the neighborhood attributes on household bicycle ownership from spurious association due to residential self-selection effects. The joint model accounts for residential self-selection due to both observed socio-demographic characteristics and unobserved preferences. In addition, the model allows for differential residential self-selection effects across different socio-demographic segments. The model is estimated using a sample of more than 5000 households from the San Francisco Bay Area. Further, a policy simulation analysis is carried out to estimate the impact of neighborhood characteristics and socio-demographics on bicycle ownership. The model results show a substantial presence of residential self-selection effects due to observed socio-demographics such as number of children, dwelling type, and house ownership. It is shown for the first time in the self-selection literature that ignoring such observed self selection effects may not always lead to overestimation of the impact of neighborhood attributes on travel related choices such as bicycle ownership. In the current context, ignoring selfselection due to socio-demographic attributes resulted in an underestimation of the impact of neighborhood attributes on bicycle ownership. In the context of unobserved factors, no significant self-selection effects were found. However, it is recommended to test for such effects as well as heterogeneity in such effects before concluding that there are no unobserved factors contributing to residential self-selection.
Modeling Demographic and Unobserved Heterogeneity in Air Passengers' Sensitivity to Service Attributes in Itinerary Choice
(National Academy of Sciences, 2006) Warburg, Valdemar; Bhat, Chandra R.; Adler, Thomas
Modeling passengers' flight choice behavior is valuable to understanding the increasingly competitive airline market and predicting air travel demands. This paper estimates standard and mixed multinomial logit models of itinerary choice for business travel, based on a stated preference survey conducted in 2001. The results suggest that observed demographic and trip related differences get incorrectly manifested as unobserved heterogeneity in a random coefficients mixed logit model that ignores demographic and trip-related characteristics of travelers. Among demographics, gender and income level have the most noticeable effects on sensitivity to service attributes in itinerary choice behavior, but frequent flyer membership, employment status, travel frequency, and group travel also emerge as important determinants. However, there is significant residual heterogeneity due to unobserved factors even after accommodating sensitivity variations due to demographic and trip-related factors. Consequently, substitution rates for each service attribute show substantial variations in the willingness-to-pay among observationally identical business passengers.
The modeling of household vehicle type choice accommodating spatial dependence effects
(Transportation Research Board of the National Academies, 2013) Paleti, Rajesh; Bhat, Chandra R.; Pendyala, Ram M.; Goulias, Konstadinos G.
Household vehicle ownership and fleet composition are choice dimensions that have important implications for policy making, particularly in the energy and environmental sustainability arena. In the context of household vehicle ownership and type choice, it is conceivable that there are substantial spatial interaction effects due to both observed and unobserved factors. This paper presents a multinomial probit model formulation that incorporates spatial spillover effects arising from both observed and unobserved factors. The model is estimated on the California add-on data set of the 2009 National Household Travel Survey. Model estimation results show that spatial dependency effects are statistically significant. The findings have important implications for model development and application in the policy forecasting arena.
A Spatial Generalized Ordered Response Model to Examine Highway Crash Injury Severity
(Elsevier, 2013) Castro, Marisol; Paleti, Rajesh; Bhat, Chandra R.
This paper proposes a flexible econometric structure for injury severity analysis at the level of individual crashes that recognizes the ordinal nature of injury severity categories, allows unobserved heterogeneity in the effects of contributing factors, as well as accommodates spatial dependencies in the injury severity levels experienced in crashes that occur close to one another in space. The modeling framework is applied to analyze the injury severity sustained in crashes occurring on highway road segments in Austin, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files from 2009 and includes a variety of crash characteristics, highway design attributes, driver and vehicle characteristics, and environmental factors. The results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. The most important determinants of injury severity on highways, according to our results, are (1) whether any vehicle occupant is ejected, (2) whether collision type is head-on, (3) whether any vehicle involved in the crash overturned, (4) whether any vehicle occupant is unrestrained by a seat-belt, and (5) whether a commercial truck is involved.
A unified mixed logit framework for modeling revealed and stated preferences: Formulation and application to congestion pricing analysis in the San Francisco Bay Area
(Elsevier, 2002) Bhat, Chandra R.; Castelar, Saul
This paper formulates and applies a unified mixed-logit framework for joint analysis of revealed and stated preference data that accommodates a flexible competition pattern across alternatives, scale difference in the revealed and stated choice contexts, heterogeneity across individuals in the intrinsic preferences for alternatives, heterogeneity across individuals in the responsiveness to level-of-service factors, state dependence of the stated choices on the revealed choice, and heterogeneity across individuals in the state dependence effect. The estimation of the mixed logit formulation is achieved using simulation techniques that employ quasi-random Monte Carlo draws. The formulation is applied to examine the travel behavior responses of San Francisco Bay Bridge users to changes in travel conditions. The data for the study are drawn from surveys conducted as part of the 1996 San Francisco Bay Area Travel Study. The results of the mixed logit formulation are compared with those of more restrictive structures on the basis of parameter estimates, implied trade-offs among level-of-service attributes, heterogeneity and state dependence effects, data fit, and substantive implications of congestion pricing policy simulations.