Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings
Sustainable Transportation & Urban Development
Maria Bassil, Ph.D. candidate
Civil Engineer
The University of Texas at Austin
Austin, Texas, United States
Carolina Baumanis, M.S.C.E.
Research Engineering/Scientist Associate & Ph.D. Candidate
University of Texas at Austin
Austin, Texas, United States
Heidi W. Ross, P.E.
Strategic Planning Manager
UT Austin Center for transportation research
Austin, Texas, United States
Randy Machemehl, Ph.D., P.E.
Professor
UT Austin
Austin, Texas, United States
Maria Bassil, Ph.D. candidate
Civil Engineer
The University of Texas at Austin
Austin, Texas, United States
Maria Bassil, Ph.D. candidate
Civil Engineer
The University of Texas at Austin
Austin, Texas, United States
This paper aims to understand the post-COVID-19 travel patterns at The University of Texas at Austin (UT Austin). It constitutes a continuation effort of the 2022 study based on a travel preference survey distributed to the whole UT community during the Spring of 2022. The 2022 travel preference survey was redistributed during the Spring of 2023 to understand how travel patterns are evolving post-COVID-19. The survey questions include information about frequency, time, and purpose of commute as well as the mode of transportation used. They aim to analyze the shift in travel behavior as we gradually move to the new normal for operating conditions. The results of both surveys are compared and analyzed to help the Office of Sustainability at the University of Texas at Austin make informed decision regarding University transportation needs. Both surveys clearly illustrate the persistence of the hybrid mode of operation: 40% of undergraduate students, 40% of graduate students 32% of faculty, and 27% of staff commuted 5 days a week to campus in 2023. Respondents expressed their tendency to use the car mode as it is the fastest way to get to campus and due to the lack of transit options available to them. This effort will help researchers and decision makers better develop travel demand modeling assumptions.