  This paper presents a novel approach to analyze quasiperiodically driven
dynamical systems. It aims to develop a complete data-driven framework for
modeling such unknown dynamics. To achieve this, we characterize Koopman
eigenfrequencies as generating frequencies of the quasiperiodic driver of the
system. We compute true eigenfrequencies of Koopman operators by applying the
theory of Reproducing Kernel Hibert Space (RKHS) and results from ergodic
theory. We also demonstrate the decomposition of quasiperiodically driven
dynamics into two components, i) the quasiperiodic driving source with
generating frequencies and ii) the driven nonlinear dynamics. A unique aspect
of the proposed framework is that it applies to the analysis of systems where
the periodic component is either non-dominant or even absent. As a case study,
we analyze a system of nine traffic signalized intersections. The proposed
framework accurately reconstructs the measured queue lengths of the signalized
intersections and makes stable long-term predictions.
