The study of the mode choice for urban regions has increased, with a growing set of recent works using joint
methodologies of data gathering and modeling with machine learning models. This work details the design and
application of a mobility survey to a private urban university in the north of Mexico. Decision tree based, machine
learning models for multiclass classification, are shown to be effective with datasets in which categorical data
predominates, having a better performance than the widely applied econometric models covered in literature. The
interpretability of decision trees helps to identify relevant variables that influence modal choice. It can be concluded
that, for the studied sample, people’s awareness of their access to collective modes is the most decisive factor, and
thus the efforts of institutions to promote investments and availability of better modes will determine the mode’s
adoption rate.

Keywords
Discrete choice model, Transportation, Mode choice study, Machine learning