Research
Three threads connecting classical traffic flow theory to modern vehicle and AI capabilities.
The MOTIF Lab works at the intersection of self-driving technologies, electric vehicles, and AI. Our research program is organized around three threads that reinforce one another:
- Theory disciplines what we expect new vehicles and AI tools to do, and where they will fail.
- Vehicle technology is reshaping the assumptions classical traffic theory was built on — fixed reaction times, identical drivers, deterministic car-following — so we re-examine those assumptions empirically.
- AI broadens the data we can use to test both, turning low-cost cameras and naturalistic driving streams into operational signals.
Pick a thread below to read more, or skip to the publications and projects for concrete results.
Traffic flow theory
Macroscopic and microscopic models of how vehicles aggregate into flow, congestion, and breakdown.
Read more →Vehicle technologies and traffic impact
How ADAS, lane-keeping assist, and electric powertrains change traffic efficiency and safety at the system level.
Read more →AI for mobility
Computer vision and machine learning for traffic monitoring, road hazard detection, and operations support.
Read more →