  To improve the traveling experience, researchers have been analyzing the role
of attitudes in travel behavior modeling. Although most researchers use
closed-ended surveys, the appropriate method to measure attitudes is debatable.
Topic Modeling could significantly reduce the time to extract information from
open-ended responses and eliminate subjective bias, thereby alleviating analyst
concerns. Our research uses Topic Modeling to extract information from
open-ended questions and compare its performance with closed-ended responses.
Furthermore, some respondents might prefer answering questions using their
preferred questionnaire type. So, we propose a modeling framework that allows
respondents to use their preferred questionnaire type to answer the survey and
enable analysts to use the modeling frameworks of their choice to predict
behavior. We demonstrate this using a dataset collected from the USA that
measures the intention to use Autonomous Vehicles for commute trips.
Respondents were presented with alternative questionnaire versions (open- and
closed- ended). Since our objective was also to compare the performance of
alternative questionnaire versions, the survey was designed to eliminate
influences resulting from statements, behavioral framework, and the choice
experiment. Results indicate the suitability of using Topic Modeling to extract
information from open-ended responses; however, the models estimated using the
closed-ended questions perform better compared to them. Besides, the proposed
model performs better compared to the models used currently. Furthermore, our
proposed framework will allow respondents to choose the questionnaire type to
answer, which could be particularly beneficial to them when using voice-based
surveys.
