Practical solutions to improve transportation systems
The University of Iowa's Initiative for Multimodal Logistics Optimization (IMLO) is a comprehensive research center with real-world impact. By turning advanced technologies like AI and machine learning into practical, deployable transportation solutions, IMLO is improving the mobility of people and goods, including in rural and underserved areas. Our three-tiered approach moves from short-term deployable solutions to long-term multimodal systems that have the potential to transform the industry.
Human-algorithm integration
Use machine learning and optimization to improve current delivery, transit, and safety issues. Examples: Predicting missed medical trips, reducing parking search time, identifying crash risks.
Human-automation integration
Advance automonous vehicles (GAVs) and drones (UAVs) through pilot testing and real-world data. Examples: Combined passenger and package transport, GPS-independent drone navigation, driverless return systems.
Multimodal systems
Focus on network-level transportation systems to coordinate passenger and freight across models. Examples: Autonomous freight networks, hub-and-spoke transit optimization, car-sharing systems, returns optimizations.
Initiative members
Beste Basciftci
Ann Melissa Campbell
Venanzio Cichella
Renato De Matta
Paul F. Hanley
Mojtaba Hosseini
Oi Luo
Jeffrey W. Ohlmann
Adam Pollack
Logistics & transportation news
UI logistics study: Package delivery drivers should walk more, drive less
Tippie researcher awarded $272K to study transportation systems
Beste Basciftci awarded $272K to study transportation systems
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