# Smoothing Traffic Flow via Control of Autonomous Vehicles

arXiv: https://arxiv.org/abs/1812.09544 · HTML: https://mlsanity.com/paper/1812.09544/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning](https://mlsanity.com/paper/2311.12261/) (2023)
- [Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control](https://mlsanity.com/paper/2408.17380/) (2024)
- [Proximal Policy Optimization Learning based Control of Congested Freeway Traffic](https://mlsanity.com/paper/2204.05627/) (2022)
- [Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning](https://mlsanity.com/paper/2412.03925/) (2024)
- [Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow](https://mlsanity.com/paper/2304.12821/) (2023)
- [Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control](https://mlsanity.com/paper/2502.00043/) (2025)

## Abstract (arXiv)

The emergence of autonomous vehicles is expected to revolutionize road transportation in the near future. Although large-scale numerical simulations and small-scale experiments have shown promising results, a comprehensive theoretical understanding to smooth traffic flow via autonomous vehicles is lacking. In this paper, from a control-theoretic perspective, we establish analytical results on the controllability, stabilizability, and reachability of a mixed traffic system consisting of human-driven vehicles and autonomous vehicles in a ring road. We show that the mixed traffic system is not completely controllable, but is stabilizable, indicating that autonomous vehicles can not only suppress unstable traffic waves but also guide the traffic flow to a higher speed. Accordingly, we establish the maximum traffic speed achievable via controlling autonomous vehicles. Numerical results show that the traffic speed can be increased by over 6% when there are only 5% autonomous vehicles. We also design an optimal control strategy for autonomous vehicles to actively dampen undesirable perturbations. These theoretical findings validate the high potential of autonomous vehicles to smooth traffic flow.