Urban traffic congestion control: a DeePC change

Alessio Rimoldi; Carlo Cenedese; Alberto Padoan; Florian Dörfler; John Lygeros

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arXiv · 2311.09851

Urban traffic congestion control: a DeePC change

Alessio Rimoldi·Carlo Cenedese·Alberto Padoan·Florian Dörfler·John Lygeros

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Abstract

Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers. By leveraging behavioral system theory and data-driven control, this paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights. To validate our approach, we consider a high-fidelity case study using the state-of-the-art simulation software package Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$_2$ emissions, demonstrating its potential for effective traffic management

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Alessio Rimoldi, Carlo Cenedese, Alberto Padoan, Florian Dörfler, John Lygeros. 2023-11-16. Urban traffic congestion control: a DeePC change. https://arxiv.org/abs/2311.09851

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