mlsanityData to 14 Oct 2026

Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control

arXiv 2412.02520 ↗First posted 3 Dec 2024cs.MA cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. Traditional practice relies on Eulerian variable speed limits (VSL) which regulate traffic through roadside signs, but suffer from infrequent updates and limited driver compliance. Recent research explored Lagrangian strategies that directly control individual vehicles, offering high reactivity and compliance, yet in realistic multi-lane settings they depend on drivers' latent lane-change intentions, making robust vehicle-level decisions difficult. Hence, we propose an Eulerian control system optimized through reinforcement learning, that (i) leverages ACC for reactivity and compliance, and (ii) obviates dependence on latent driver intentions by regulating aggregate density near bottlenecks, crucially via headway commands rather than speed commands. We evaluate two variants of our system, time-headway and distance-headway control, in large-scale simulations across a range of traffic conditions. Both variants outperform baselines, improving traffic flow by up to 10.6% over human traffic and 6.7% over traditional VSL. To strengthen evaluation, we propose a novel boundary-aware speed metric addressing a recognized flaw in simulation studies with dynamic vehicle entry and exit. The empirical results, together with our emphasis on deployable system design, suggest a path towards practical, safe, and scalable highway congestion mitigation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 35 references that are in the map. First posted on arXiv 3 Dec 2024.

Builds on

8 of 35 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  3. 2017Dissipation of stop-and-go waves via control of autonomous vehicles: Field experimentscited by 45
  4. 2023I-24 MOTION: An instrument for freeway traffic sciencecited by 26
  5. 2020A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learningcited by 25
  6. 2021Scalable Multiagent Driving Policies For Reducing Traffic Congestioncited by 11
  7. 2021Learning a Robust Multiagent Driving Policy for Traffic Congestion Reductioncited by 4
  8. 2024Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Trafficcited by 1

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  1. 2024Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles
  2. 2025Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks
  3. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  4. 2023Traffic Shaping and Hysteresis Mitigation Using Deep Reinforcement Learning in a Connected Driving Environment
  5. 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
  6. 2024CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control
  7. 2023Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging
  8. 2024Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data