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

Rajesh Paleti, Lacramioara Balan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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 misclassification probabilities across different population segments. Models that ignore misclassification 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 activity-based models.

Original languageEnglish (US)
Pages (from-to)1467-1485
Number of pages19
JournalTransportation
Volume46
Issue number4
DOIs
StatePublished - Aug 1 2019

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ownership
travel
modeling
transportation policy
study method
travel demand
transportation planning
policy analysis
econometrics
elasticity
Elasticity
Planning
planning
household
decision
demand
evidence
method

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Development
  • Transportation

Cite this

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abstract = "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 misclassification probabilities across different population segments. Models that ignore misclassification 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 activity-based models.",
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Misclassification in travel surveys and implications to choice modeling : application to household auto ownership decisions. / Paleti, Rajesh; Balan, Lacramioara.

In: Transportation, Vol. 46, No. 4, 01.08.2019, p. 1467-1485.

Research output: Contribution to journalArticle

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AU - Balan, Lacramioara

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