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

翻译：城市交通拥堵在快速扩张的城市中仍是严峻挑战，尽管数据充足且政策制定者已付出努力。本文利用行为系统理论与数据驱动控制，将DeePC算法应用于动态交通信号灯执行的城市交通控制场景。为验证该方法，我们采用先进的交通仿真软件包SUMO（Simulation of Urban MObility）构建高保真案例研究。初步结果表明，DeePC在出行时间、二氧化碳排放等多项关键指标上均优于现有方法，彰显其有效管理交通的潜力。