# Urban traffic congestion control: a DeePC change

arXiv: https://arxiv.org/abs/2311.09851 · HTML: https://mlsanity.com/paper/2311.09851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Data-Enabled Predictive Control: In the Shallows of the DeePC](https://mlsanity.com/paper/1811.05890/) (2018)
- [DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow](https://mlsanity.com/paper/2203.10639/) (2022)
- [Linear tracking MPC for nonlinear systems Part II: The data-driven case](https://mlsanity.com/paper/2105.08567/) (2021)
- [Incentive-Based Electric Vehicle Charging for Managing Bottleneck Congestion](https://mlsanity.com/paper/2111.05600/) (2021)
- [Data-Enabled Predictive Control for Grid-Connected Power Converters](https://mlsanity.com/paper/1903.07339/) (2019)
- [Behavioral uncertainty quantification for data-driven control](https://mlsanity.com/paper/2204.02671/) (2022)
- [Data-driven representations of conical, convex, and affine behaviors](https://mlsanity.com/paper/2310.15354/) (2023)
- [Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic](https://mlsanity.com/paper/2204.03747/) (2022)
- [Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype algorithm](https://mlsanity.com/paper/2304.12004/) (2023)

## Built on by (most cited first, 1 of 1)

- [Data-driven generalized perimeter control: Z"urich case study](https://mlsanity.com/paper/2603.16599/) (2026)

## Abstract (arXiv)

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 CO2 emissions, demonstrating its potential for effective traffic management