The unpredictable elements involved in a vehicular traffic system, like human interaction and
weather, lead to a very complicated, high-dimensional, nonlinear dynamical system …

We consider the application of Koopman theory to nonlinear partial differential equations
and data‐driven spatio‐temporal systems. We demonstrate that the observables chosen for …

The dynamic mode decomposition (DMD)—a popular method for performing data-driven
Koopman spectral analysis—has gained increased popularity for extracting dynamically …

The Koopman operator is a linear but infinite-dimensional operator that governs the
evolution of scalar observables defined on the state space of an autonomous dynamical …

Koopman operator theory shows how nonlinear dynamical systems can be represented as
an infinite-dimensional, linear operator acting on a Hilbert space of observables of the …

Spectral decomposition of the Koopman operator is attracting attention as a tool for the
analysis of nonlinear dynamical systems. Dynamic mode decomposition is a popular …

Many highways are acquiring smart transportation systems to improve traffic efficiency,
safety and management. Intelligent transportation systems can monitor traffic congestion by …

We present a framework for feature extraction and mode decomposition of spatiotemporal
data generated by ergodic dynamical systems. Unlike feature extraction techniques based …

Dynamic mode decomposition (DMD) is an Arnoldi-like method based on the Koopman
operator. It analyzes empirical data, typically generated by nonlinear dynamics, and …

Urban road transportation performance is the result of a complex interplay between the
network supply and the travel demand. Fortunately, the framework around the macroscopic …