Data-driven generalized perimeter control: Z\"urich case study

TL;DR

This paper introduces a data-driven control method for urban traffic management using behavioral systems theory and predictive control, validated on a high-fidelity Zurich city simulation, reducing travel time and emissions.

Contribution

It presents a novel traffic control approach that combines behavioral systems theory with data-enabled predictive control, addressing data sparsity and constraint enforcement.

Findings

Significant reduction in total travel time.

Lower CO2 emissions in simulation.

Effective control in high-fidelity city model.

Abstract

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 Z\"urich, 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.