2018/11/06 by Gianluca Bianchin, Fabio Pasqualetti, Bianchin, Gianluca +1 · 1 citation
Computer Science · Engineering · #Data Visualization and Analytics #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Traffic control and management #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1811.02673
openalex publication_date 2018/11/06 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/29
This paper proposes a simplified version of classical models for urban\ntransportation networks, and studies the problem of controlling intersections\nwith the goal of optimizing network-wide congestion. Differently from\ntraditional approaches to control traffic signaling, a simplified framework\nallows for a more tractable analysis of the network overall dynamics, and\nenables the design of critical parameters while considering network-wide\nmeasures of efficiency. Motivated by the increasing availability of real-time\nhigh-resolution traffic data, we cast an optimization problem that formalizes\nthe goal of minimizing the overall network congestion by optimally controlling\nthe durations of green lights at intersections. Our formulation allows us to\nrelate congestion objectives with the problem of optimizing a metric of\ncontrollability of an associated dynamical network. We then provide a technique\nto efficiently solve the optimization by parallelizing the computation among a\ngroup of distributed agents. Lastly, we assess the benefits of the proposed\nmodeling and optimization framework through microscopic simulations on typical\ntraffic commute scenarios for the area of Manhattan. The optimization framework\nproposed in this study is made available online on a Sumo microscopic simulator\nbased interface [1].\n