Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control

[2412.02520] Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control Skip to main content Search Submit Donate Log in Computer Science > Multiagent Systems arXiv:2412.02520 (cs) [Submitted on 3 Dec 2024 ( v1 ), last revised 22 Sep 2026 (this version, v5)] Title: Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control Authors: Yaron Veksler , Sharon Hornstein , Han Wang , Maria Laura Delle Monache , Daniel Urieli View PDF HTML (experimental) Abstract: 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. Comments: Accepted as a full paper to the 29th International Conference on Intelligent Transportation Systems (ITSC), 2026. Website: this https URL Subjects: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY) Cite as: arXiv:2412.02520 [cs.MA] (or arXiv:2412.02520v5 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2412.02520 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yaron Veksler [ view email ] [v1] Tue, 3 Dec 2024 16:13:42 UTC (2,766 KB) [v2] Sun, 26 Jan 2025 09:22:25 UTC (4,376 KB) [v3] Sun, 2 Feb 2025 08:49:21 UTC (4,376 KB) [v4] Sun, 23 Aug 2026 07:27:01 UTC (511 KB) [v5] Tue, 22 Sep 2026 13:36:38 UTC (511 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.MA < prev | next > new | recent | 2024-12 Change to browse by: cs cs.AI cs.LG cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control#670307
• Statistical Reinforcement Learning - Modern Machine Learning Approaches.#489461
• Congestion Control Principles#134883
• Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids#381778
• Expert Behavior Prior Reinforcement Learning#665874