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This paper proposes a simplified version of classical models for urban traffic networks and studies the problem of optimizing the network overall efficiency by controlling the signalized intersections. Differently from traditional approaches to control traffic signaling, the tractability of our framework allows us to effectively model large-scale interconnections 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 optimizing vehicle evacuation by controlling the durations of green lights at the intersections under the current congestion conditions. Our framework allows us to relate efficiency objectives with the optimization of a metric of controllability of the associated dynamical network. We then provide a technique to efficiently solve the optimization by parallelizing the computation among a group of distributed agents. Last, we assess the benefits of the proposed modeling and optimization framework through macroscopic and microscopic simulations on daily commute scenarios for the urban interconnection of Manhattan, NY, USA.

Gramian-Based Optimization for the Analysis and Control of Traffic Networks