Graduation Year


Document Type




Degree Granting Department

Civil Engineering

Major Professor

Ram M. Pendyala, Ph.D.

Committee Member

Steven E. Polzin, Ph.D., P.E.

Committee Member

Jian J. Lu, Ph.D., P.E.


Activity based travel demand modeling, travel behavior, household travel survey, stochastic frontier models, comparisons of behavior


Recent developments in microsimulation modeling of activity and travel demand have called for the explicit recognition of time-space constraints under which individuals perform their activity and travel patterns. The estimation of time-space prism vertex locations, i.e., the perceived time constraints, is an important development in this context. Stochastic frontier modeling methodology offers a suitable framework for modeling and identifying the expected vertex locations of time space prisms within which people execute activity-travel patterns. In this work, stochastic frontier models of time space prism vertex locations are estimated for samples drawn from a household travel survey conducted in 2001 in the city of Thane on the west coast of India and National Household Travel Survey 2001, United States. This offers an opportunity to study time constraints governing activity travel patterns of individuals in a developing as well as developed country context. The work also includes comparisons between males and females, workers and non-workers, and developed and developing country contexts to better understand how socio-economic and socio-cultural norms and characteristics affect time space prism constraints. It is found that time space prism constraints in developing country data set can be modeled using the stochastic frontier modeling methodology. It is also found that significant differences exist between workers and non-workers and between males and females,possibly due to the more traditional gender and working status roles in the Indian context. Finally, both differences and similarities were noticed when comparisons were made between results obtained from the data set of India and United States. Many of these differences can be explained by the presence of other constraints including institutional, household, income, and transportation accessibility constraints that are generally significantly greater in the developing country context.