  Traffic flow modeling is typically performed at one of three different scales
(microscopic, mesoscopic, or macroscopic), each with distinct modeling
approaches. Recent works that attempt to merge models at different scales have
yielded some success, but there still exists a need for a single modeling
framework that can seamlessly model traffic flow across several spatiotemporal
scales. The presented work utilizes a renormalization group (RG) theoretic
approach, building upon our prior research on statistical mechanics-inspired
traffic flow modeling. Specifically, we use an Ising model-inspired cellular
automata model to represent traffic flow dynamics. RG transformations are
applied to this model to obtain coarse-grained parameters (interaction and
field coefficients) to simulate traffic at coarser spatiotemporal scales and
different vehicular densities. We measure the accuracy of the coarse-grained
traffic flow simulation using a pixel-based image correlation metric and find
good correlation between the dynamics at different scales. Importantly,
emergent traffic dynamics such as backward moving congestion waves are retained
at coarser scales with this approach. The presented work has the potential to
spur the development of a unified traffic flow modeling framework for
transportation analysis across varied spatiotemporal scales, while retaining an
analytical relationship between the model parameters at these scales.
