Research Scientist
Texas A&M Transportation Institute
Austin, TX, United States
Dr. Ipek Nese Sener is a Research Scientist in TTI’s Mobility Division, with around 20 years of experience in the field of transportation, where her research focus examines the intersection of social and behavioral sciences and brings together the elements of mobility, safety, equity, health, and technology. She has a particular interest in transportation-based health research as well as understanding the role of active, alternative, and emerging modes of transportation in the overall transportation ecosystem. She has studied extensively issues related to accessibility, affordability, equity, and inclusion and has led various interdisciplinary and data-driven studies examining individuals’ decisions and activity-travel patterns, the changing nature of transportation choices, and the related impact on and/or connection to wellbeing, as well as sustainable and equitable mobility.
Dr. Sener is a prolific technical author and an internationally recognized researcher in the field, with more than 125 publications, and presented and moderated at national and international conferences, panels, and workshops. She serves in the graduate committee faculty of Texas A&M University and is a faculty affiliate at the Texas A&M Center for Population Health and Aging. She is also on the board of directors of the International Professional Association for Transport and Health as well as Feonix—Mobility Rising, a non-profit organization dedicated to supporting mobility for vulnerable and underserved populations. She is deeply involved with the Transportation Research Board (TRB), serves as a handling editor for the Transportation Research Record journal, and has been an active member of TRB committees, including the ‘Transportation and Public Health’ Committee, the ‘Travel Behavior and Values’ Committee, and the ‘Effects of Information and Communication Technologies on Travel Choices’ Committee.
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Sketch Planning Level Shared Use Path User Volume Estimation using Machine Learning
Sunday, June 16, 2024
1:15 PM - 2:30 PM ET