🤖 AI Summary
To address traffic congestion on signal-free suburban freeways exacerbated by growing commuter demand and constrained infrastructure, this paper proposes a vehicle-mounted distributed speed control protocol based on physics-informed reinforcement learning (PIL-RL). Methodologically, we introduce a novel corridor-level abstraction modeling framework that integrates macroscopic traffic flow dynamics, gap-acceptance theory, and high-fidelity microscopic simulation (PTV Vissim), embedding emergent traffic phenomena directly into the proximal policy optimization (PPO) algorithm. This enables fully decentralized, real-time velocity optimization with strong generalization across heterogeneous traffic conditions. Experimental evaluation on realistic network simulations demonstrates a 5% increase in total throughput, a 13% reduction in average delay, and a 3% decrease in stop occurrences—substantially improving traffic flow smoothness and robustness against congestion onset—without requiring any new physical infrastructure.
📝 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.