Urban traffic congestion control: a DeePC change

• URL: http://arxiv.org/abs/2311.09851v1
• Date: Thu, 16 Nov 2023 12:26:55 GMT
• Title: Urban traffic congestion control: a DeePC change
• Authors: Alessio Rimoldi, Carlo Cenedese, Alberto Padoan, Florian D\"orfler, John Lygeros
• Abstract summary: This paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights. Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$$ emissions.
• Score: 3.0023888160895473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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