t Travel surveys that elicit responses to questions regarding daily activity and
travel choices form the basis for most of the transportation planning and policy analysis.
The response variables collected in these surveys are prone to errors leading to mismeasurement
or misclassification. Standard modeling methods that ignore these errors while
modeling travel choices can lead to biased parameter estimates. In this study, methods
available in the econometrics literature were used to quantify and assess the impact of
misclassification errors in auto ownership choice data. The results uncovered significant
misclassification rates ranging from 1 to 40% for different auto ownership alternatives.
Also, the results from latent class models provide evidence for variation in misclassifi-
cation probabilities across different population segments. Models that ignore misclassifi-
cation were not only found to have lower statistical fit but also significantly different
elasticity effects for choice alternatives with high misclassification probabilities. The
methods developed in this study can be extended to analyze misclassification in several
response variables (e.g., mode choice, activity purpose, trip/tour frequency, and mileage)
that constitute the core of advanced travel demand models including tour and activitybased
models.

• Misclassification in travel surveys and implications to choice modeling: application to household auto ownership decisions