Urban traffic congestion control: a DeePC change
Systems and Control
2023-11-17 v1 Systems and Control
Optimization and Control
Machine Learning
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 emissions, demonstrating its potential for effective traffic management
Cite
@article{arxiv.2311.09851,
title = {Urban traffic congestion control: a DeePC change},
author = {Alessio Rimoldi and Carlo Cenedese and Alberto Padoan and Florian Dörfler and John Lygeros},
journal= {arXiv preprint arXiv:2311.09851},
year = {2023}
}
Comments
This paper has been submitted to IEEE ECC24