Abstract
Autonomous vehicles, AVs, as an emerging technology will contribute to fundamental changes in the transportation of cities. Medium-sized academic-dominated U.S. towns with cold winters and a large proportion of college students and employees are some of the regions with special travel patterns that may potentially contribute to a different perception of AVs. Using a stated preference survey in the two main universities in the Fargo-Moorhead area, perceived usefulness, perceived risk, and a winter-related variable defined as transportation safety improvement by AVs in inclement weather were simultaneously modeled. Given that the respondents agreed with the stated usefulness and risks of AVs, not being single was associated with an increased likelihood of agreeing with the winter-related variable for the respondents above 25. Low income households were associated with a 25% increase in the likelihood of agreeing with the winter-related variable for the respondents in the non-student model.
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Babak Mirzazadeh
Babak Mirzazadeh is a PhD candidate in Civil Engineering at North Dakota State University and a Graduate Research Assistant in the Upper Great Plains Transportation Institute. His research focus is econometric modeling, traditional and activity-based travel demand modeling, and travel behavior during the era of autonomous vehicles.
Ying Huang
Ying Huang received the Ph.D. degree from the Missouri University of Science and Technology in 2012. She is currently an Associate Professor in the Department of Civil, Construction, and Environmental Engineering, North Dakota State University. Her research interests include smart cities, corrosion mitigation and assessment, and emergency evacuation in multi-hazard.
Diomo Motuba
Diomo Motuba is an assistant professor with the Transportation Logistics and Finance Department at North Dakota State University. He is an associate research fellow with the Upper Great Plains Transportation Institute. His research interests are travel demand modeling, land use models, freight modeling, transportation economics, and artificial intelligence applications.