Data-driven generalized perimeter control: Zürich case study
This addresses traffic management for cities, offering a data-efficient alternative to model-based approaches, though it appears incremental in applying existing control theory to traffic.
The paper tackled urban traffic congestion by proposing a data-driven predictive control method for traffic lights, validated in a high-fidelity simulation of Zürich, resulting in improvements in total travel time and CO2 emissions.
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.