Research Projects

Traffic flow modeling is typically performed at distinct scales: microscopic (that models individual vehicles), mesoscopic (that models clusters of vehicles) and macroscopic (that models traffic as a fluid flow). Creating a single modeling framework for traffic flow can not only simplify analysis and reduce computational effort, but it could also provide an opportunity to select the modeling scale that best fits a desired goal, such as prediction of congestion, platooning of connected autonomous vehicles or determining fuel efficient driving behaviors. Our physics-inspired work borrows from the field of statistical mechanics and **renormalization group theory**. We have shown that using just two vehicle-interaction and traffic flow parameters, we can perform systematic transformation from one scale to another while maintaining fidelity across multiple (but not arbitrarily large) spatial and time scales.