  This paper proposes Koopman operator theory and the related algorithm
dynamical mode decomposition (DMD) for analysis and control of signalized
traffic flow networks. DMD provides a model-free approach for representing
complex oscillatory dynamics from measured data, and we study its application
to several problems in signalized traffic. We first study a single signalized
intersection, and we propose applying this method to infer traffic signal
control parameters such as phase timing directly from traffic flow data. Next,
we propose using the oscillatory modes of the Koopman operator, approximated
with DMD, for early identification of unstable queue growth that has the
potential to cause cascading congestion. Then we demonstrate how DMD can be
coupled with knowledge of the traffic signal control status to determine
traffic signal control parameters that are able to reduce queue lengths.
Lastly, we demonstrate that DMD allows for determining the structure and the
strength of interactions in a network of signalized intersections. All examples
are demonstrated using a case study network instrumented with high resolution
traffic flow sensors.
