Urban traffic congestion control: a DeePC change

TL;DR

This paper applies the DeePC data-driven control algorithm to urban traffic light management, demonstrating its potential to reduce congestion, travel time, and emissions through high-fidelity simulation validation.

Contribution

It introduces the use of the DeePC algorithm for urban traffic control, leveraging behavioral system theory and data-driven methods in this context.

Findings

DeePC outperforms existing traffic control methods in simulations.

Significant reductions in travel time and CO2 emissions achieved.

Validated using high-fidelity SUMO simulations.

Abstract

Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers. By leveraging behavioral system theory and data-driven control, this paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights. To validate our approach, we consider a high-fidelity case study using the state-of-the-art simulation software package Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$_2$ emissions, demonstrating its potential for effective traffic management