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

TL;DR

This paper presents a reinforcement learning-based protocol for self-regulating cars that dynamically modulate speeds to optimize traffic flow and prevent congestion in free-flow highway networks without new infrastructure.

Contribution

It introduces a physics-informed RL framework that learns robust speed modulation policies from traffic observations, integrating classical traffic theory with microscopic simulation.

Findings

Increases throughput by 5% in simulations

Reduces average delay by 13%

Decreases total stops by 3%

Abstract

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.