# Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control

arXiv: https://arxiv.org/abs/2412.02520 · HTML: https://mlsanity.com/paper/2412.02520/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning](https://mlsanity.com/paper/2007.05156/) (2020)
- [Scalable Multiagent Driving Policies For Reducing Traffic Congestion](https://mlsanity.com/paper/2103.00058/) (2021)
- [Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction](https://mlsanity.com/paper/2112.03759/) (2021)
- [Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic](https://mlsanity.com/paper/2402.16993/) (2024)

## 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.