Alireza KhaniView profile
Associate Professor
Alireza Khani is an Associate Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota's College of Engineering. His research focuses on modeling the impacts of emerging technologies on public transit systems and optimizing these systems for greater efficiency and reliability. Dr. Khani actively contributes to transportation research through numerous publications and substantial grant-funded projects. Dr. Khani's research interests center on transportation engineering with a particular emphasis on public transit systems. His work utilizes network modeling and optimization techniques to integrate public transit with autonomous mobility-on-demand services and electric fleets using renewable energy. His research spans urban mobility challenges, focusing on sustainable transportation solutions that address both passenger and freight transportation needs. His work increasingly incorporates machine learning techniques to analyze travel behavior and optimize transportation networks. His recent publications demonstrate a strong focus on electric bus systems, autonomous mobility-on-demand services, and optimization of transit networks. The research trends show progression from traditional transit modeling to integrating cutting-edge technologies like AI algorithms and autonomous vehicles. His work addresses critical transportation challenges including last-mile connectivity, rural transit solutions, and the transition to electric fleets, with growing emphasis on data-driven approaches to transportation planning. Dr. Khani actively mentors students and seeks undergraduate researchers for software development projects related to transportation systems. His research is supported by multiple substantial grants including projects funded by the National Science Foundation and the Minnesota Department of Transportation. Current projects include MobiliSlice: A Personalized Car-sharing System for Smart Urban Mobility , Estimating Likely Mode Shift and VMT Reduction Potential using TBI Data and AI Algorithms , and Transitioning to EV Fleets: Best Practices and A Decision Tool . Dr. Khani leads the UMN Transit research group, which focuses on developing mathematical, computational, and data analytic tools to evaluate and optimize transit systems. The group's research activities center on creating innovative solutions for modern transportation challenges, particularly those involving the integration of emerging technologies with traditional public transit infrastructure. The lab maintains strong connections with transportation agencies and actively contributes to practical solutions for real-world transit problems.








