  This paper explores a novel data-driven approach based on recent developments
in Koopman operator theory and dynamic mode decomposition (DMD) for modeling
signalized intersections. Vehicular flow and queue formation on signalized
intersections have complex nonlinear dynamics, making system identification,
modeling, and controller design tasks challenging. We employ a Koopman
theoretic approach to transform the original nonlinear dynamics into locally
linear infinite-dimensional dynamics. The data-driven approach relies entirely
on spatio-temporal snapshots of the traffic data. We investigate several key
aspects of the approach and provide insights into the usage of DMD-type
algorithms for application in adaptive signalized intersections. To demonstrate
the utility of the obtained linearized dynamics, we perform prediction of the
queue lengths at the intersection; and compare the results with the
state-of-the-art long short term memory (LSTM) method. The case study involves
the morning peak vehicle movements and queue lengths at two Orlando area
signalized intersections. It is observed that DMD-based algorithms are able to
capture complex dynamics with a linear approximation to a reasonable extent.
