# Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks

arXiv: https://arxiv.org/abs/2506.11973 · HTML: https://mlsanity.com/paper/2506.11973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 69 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 4 of 4)

- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning](https://mlsanity.com/paper/2104.09936/) (2021)
- [Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion](https://mlsanity.com/paper/1903.02589/) (2019)
- [Freeway Lane Management Approach in Mixed Traffic Environment with Connected Autonomous Vehicles](https://mlsanity.com/paper/1609.02946/) (2016)

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

- [Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget](https://mlsanity.com/paper/2509.00135/) (2025)

## Abstract (arXiv)

Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms, such as traffic signals or local heuristics, are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns, demonstrating its potential for scalable, ML-driven traffic management.