2023/11/16 by Alessio Rimoldi, Carlo Cenedese, Rimoldi, Alessio +7
Engineering · Social Sciences · #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Traffic Prediction and Management Techniques #Traffic control and management #Transportation Planning and Optimization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.09851
openalex publication_date 2023/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 CO2 emissions, demonstrating its potential for effective traffic management