Data-driven generalized perimeter control: Zürich case study

2026-03-17

AI Summary

This study addresses key challenges in urban traffic congestion management—namely, the high cost of traditional modeling, data sparsity, and the difficulty of enforcing hard constraints—by proposing a novel traffic signal control approach that integrates behavioral systems theory with data-driven predictive control. The method operates without requiring an explicit system model, effectively handles sparse observational data, and rigorously enforces physical and operational constraints. Validated through microscopic traffic simulations in a high-fidelity closed-loop environment replicating Zurich’s road network, the approach significantly reduces total travel time and CO₂ emissions. These results demonstrate its potential as a new paradigm for intelligent traffic control.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Behavior Learning & Control
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

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 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.

Problem

• urban traffic congestion
• data-driven control
• traffic modeling
• hard constraints
• sparse traffic data

Innovation

• behavioral systems theory
• data-enabled predictive control
• dynamic traffic light control
• microscopic traffic simulation
• urban traffic congestion