Urban traffic congestion is a key challenge for the development of modern cities, requiring advanced control techniques to optimize existing infrastructures usage. Despite the extensive availability of data, modeling such complex systems remains an expensive and time consuming step when designing model-based control approaches. On the other hand, machine learning approaches require simulations to bootstrap models, or are unable to deal with the sparse nature of traffic data and enforce hard constraints. We propose a novel formulation of traffic dynamics based on behavioral systems theory and apply data-enabled predictive control to steer traffic dynamics via dynamic traffic light control. A high-fidelity simulation of the city of Zürich, the largest closed-loop microscopic simulation of urban traffic in the literature to the best of our knowledge, is used to validate the performance of the proposed method in terms of total travel time and CO2 emissions.

翻译：城市交通拥堵是现代化城市发展面临的关键挑战，需要先进的控制技术来优化现有基础设施的利用。尽管数据资源广泛可用，但在设计基于模型的控制方法时，对此类复杂系统进行建模仍然是昂贵且耗时的步骤。另一方面，机器学习方法需要仿真来引导模型构建，或者无法处理交通数据的稀疏性并强制执行硬约束。我们基于行为系统理论提出了一种新的交通动力学表述，并应用数据驱动的预测控制通过动态交通信号灯控制来引导交通动态。利用苏黎世城市的高保真仿真（据我们所知，这是文献中最大规模的闭环微观城市交通仿真），从总行程时间和二氧化碳排放量两方面验证了所提方法的性能。