  This paper proposes a simplified version of classical models for urban
transportation networks, and studies the problem of controlling intersections
with the goal of optimizing network-wide congestion. Differently from
traditional approaches to control traffic signaling, a simplified framework
allows for a more tractable analysis of the network overall dynamics, and
enables the design of critical parameters while considering network-wide
measures of efficiency. Motivated by the increasing availability of real-time
high-resolution traffic data, we cast an optimization problem that formalizes
the goal of minimizing the overall network congestion by optimally controlling
the durations of green lights at intersections. Our formulation allows us to
relate congestion objectives with the problem of optimizing a metric of
controllability of an associated dynamical network. We then provide a technique
to efficiently solve the optimization by parallelizing the computation among a
group of distributed agents. Lastly, we assess the benefits of the proposed
modeling and optimization framework through microscopic simulations on typical
traffic commute scenarios for the area of Manhattan. The optimization framework
proposed in this study is made available online on a Sumo microscopic simulator
based interface [1].
