Advanced Travel Demand Modeling: Choice Model Estimation Training
(PI: Chandra Bhat)
Dr. Bhat serves as the PI for this ongoing North Central Texas Council of Governments (NCTCOG)
funded project.
It is important to accurately predict the vehicle ownership of households to support critical transportation
infrastructure planning and project mobile source emission levels. Household vehicle ownership is likely to vary
depending upon the demographic characteristics of the household, vehicle attributes, fuel costs, travel costs,
and the physical environment characteristics (land-use and urban form attributes) of the residential neighborhood.
Thus, the substantial changes in the demographic characteristics of households and individuals projected in the next
decade and beyond can have a significant impact on household vehicle fleet holdings and usage. Similarly,
the direct and demographic interaction effects of vehicle attributes, fuel costs, travel costs, and
neighborhood characteristics are also likely to impact on household vehicle ownership. A clear estimate of
such impacts will not only help accurate predictions, but can also inform the design of proactive land-use,
economic, and transportation policies to influence household vehicle holdings and usage in a way that reduces
traffic congestion and air quality problems. A relatively more recent consideration that places emphasis on
vehicle ownership modeling is the significant fraction of human-generated greenhouse gas (GHG) emissions and
fossil fuel-based energy consumption that may be attributed to on-road vehicle travel. Policymakers at the
national, state, and regional levels are exploring transportation and land-use strategies to decrease private
vehicle ownership as a means to reduce both GHG emissions and fossil fuel dependence.
From a modeling standpoint, vehicle ownership forecasting can be undertaken using aggregate extrapolation
models which model vehicle ownership directly at the aggregate level (such as a zonal, regional or national level)
or using disaggregate vehicle ownership models that use the household as the decision making unit and obtain zonal,
regional, or national level forecasts by aggregating over households. The disaggregate models are structurally
more behavioral compared to aggregate models, and are better able to capture the causal relationship between
vehicle ownership determinants and vehicle ownership levels. Consequently, disaggregate methods have become
the preferred approach to model vehicle ownership choice. Since vehicle ownership is a categorical variable,
disaggregate auto ownership models usually take the form of multinomial logit or nested logit or more
advanced versions of these discrete choice models.
During the project, the UT team will work closely with a set of five staff members from NCTCOG to develop the
vehicle ownership model. The intent of the project is to ensure that the NCTCOG Staff become comfortable
with discrete choice model estimation in general, while at the same time producing a vehicle ownership
model for use in NCTCOG’s travel modeling framework.