School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

Advancing the Practical Integration of Attitudes into Regional Travel Demand Models: Attitudinal Marker Variables, Systematic Model Specification, and Temporal Stability of Attitudes

By Ilsu Kim

Advisor:

Dr. Patricia Mokhtarian

School of Civil and Environmental Engineering

Georgia Institute of Technology

Committee Members:

Dr. Michael Hunter

School of Civil and Environmental Engineering

Georgia Institute of Technology

Dr. Sofia Perez-Guzman

School of Civil and Environmental Engineering

School of Industrial and Systems Engineering

Georgia Institute of Technology

Dr. Subhrajit Guhathakurta

School of City and Regional Planning

Georgia Institute of Technology

Dr. Ram Pendyala

School of Sustainable Engineering and the Built Environment

Arizona State University

Date and Time: Monday, November 9, 2026, at 3:00 PM EST

Location: Mason 2119 or online via Microsoft Teams

ABSTRACT
Facing evolving work and commute patterns, shifting lifestyles, and
emerging travel options such as micromobility and self-driving vehicles, travel
demand models (TDMs) need, and will continue to need, enhanced predictive
performance. Transportation-related attitudes help explain why individuals and
households with similar socioeconomic, demographic, and residential
characteristics make different travel choices. However, integrating attitudes into
regional TDMs requires practical ways to measure them, incorporate them into
travel behavior models, and forecast their shifts over time.
The goal of this dissertation is to support the practical integration of
traveler attitudes into TDMs. The marker variable (MV) approach reduces
measurement burdens by using a small set of selected attitudinal statements.
Attitudinal MVs can be used to impute attitudinal factor scores through functions
trained on an attitude-rich donor dataset or included directly in travel behavior
models. The dissertation addresses three complementary questions: (1) What are
the types and magnitudes of benefits obtained from incorporating MV-predicted
attitudinal factor scores or MVs into various travel behavior models? (2) How can
attitudinal MVs be best leveraged to improve the performance of individual travel
behavior models within regional TDMs? and (3) How stable are transportationrelated
attitudes over time, and what factors are associated with attitudinal shifts?
The findings show that including attitudes yields meaningful benefits,
particularly in predicting less-common alternatives, and can reveal additional
significant non-attitudinal explanatory variables. Segmenting the population and
incorporating attitudes into latent class membership further improve vehicle
ownership models, especially for single-adult households, highlighting the value of
allowing model structures and attitudinal roles to vary across segments.
Attitudinal stability differs across domains, with the pro-car-ownership attitude
showing greater persistence than the pro-transit and pro-active-mode attitudes,
while within-person shifts remain largely unexplained by observed characteristics
and their changes. Overall, this dissertation supports the practical integration of
attitudes into regional TDMs by demonstrating the value of abbreviated
measurement, offering a structured model specification process, and providing
insights into temporal stability. These findings inform how attitudes can be
collected and used in modeling practice, while identifying further research needed
to assign attitudes to individuals and forecast their evolution over time.