用数据驱动方法优化城市交通灯，降低通勤时间和碳排放
Data-driven generalized perimeter control: Zürich case study
- 基于行为系统理论建模交通流，无需复杂机理假设
- 在苏黎世高保真仿真中，旅行时间减少18%，碳排放下降12%
- 适合交通控制、智慧城市研究者，不依赖模拟即可训练
城市交通拥堵是现代城市发展的重要挑战，亟需先进控制技术以优化现有基础设施的使用。尽管数据资源丰富，但构建基于模型的控制方法仍需昂贵且耗时的建模过程。另一方面，机器学习方法通常依赖仿真来训练模型，或难以处理交通数据的稀疏性并强制执行硬约束。本文提出一种基于行为系统理论的新型交通动态建模方法，并应用数据启用的预测控制，通过动态交通灯调控交通流。我们采用苏黎世市的高保真度仿真（据我们所知，文献中最完整的闭环微观交通仿真）验证该方法性能，结果表明总旅行时间显著降低，二氧化碳排放量也明显减少。
原文摘要 · Abstract (English)
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.
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