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Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control… — arXiv:2609.35152

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35152
License (as archived): CC0-1.0
Archived: 2026-10-07T22:41:08.034Z
SHA-256 of the archived text: 2d148fdf713837afd090f37e4c112664f9478a4e8dad6873ba03887fdfecd099
Stable URL of this page: https://metraipolis.com/documento/dc_cb4ebff50d3d

How to cite

Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control… — arXiv:2609.35152 — arXiv (en), https://arxiv.org/abs/2609.35152 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb4ebff50d3d (Metraipolis archive dc_cb4ebff50d3d, sha256 2d148fdf713837afd090f37e4c112664f9478a4e8dad6873ba03887fdfecd099)

BibTeX

@misc{dccb4ebff50d3d,
  title = {Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control… — arXiv:2609.35152},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35152},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb4ebff50d3d; sha256 2d148fdf713837afd090f37e4c112664f9478a4e8dad6873ba03887fdfecd099},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35152. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Successive weaving segments (SWSs) on urban expressways are bottlenecks prone to recurrent congestion and collisions, requiring fine-grained active traffic management (ATM). Existing approaches struggle to balance the adaptive performance of data-driven optimization with the resilience and transferability of model-based control. We propose a hybrid framework to coordinate lane-level variable speed limits (VSLs) and ramp metering across SWSs. First, we reconstruct L-METANET, a lane-level macroscopic traffic flow model that captures free and forced lane changes. Second, we combine XGBoost-SHAP with a random-parameters binary logit (RPBL) model to derive analytical equations for merging and diverging collision risks and formulate system cost and reward functions. Third, we develop MPC-STMAPPO, a hierarchical controller integrating model predictive control (MPC) and multi-agent reinforcement learning (MARL). Its upper MPC layer uses L-METANET for long-horizon rolling optimization and generates baseline commands; its lower spatiotemporal MAPPO (ST-MAPPO) layer, enhanced with Mamba cells and graph attention, produces residual actions for short-horizon adjustment. Real-world experiments on the 18-km Eastern Expressway in Changchun, China, show that L-METANET accurately reproduces lane-changing-induced flow redistribution and capacity drops, with state evolution aligned with ground truth. XGBoost-SHAP-RPBL achieves AUCs above 0.80 in most tasks, outperforming conventional logit models. MPC-STMAPPO converges faster and performs better across multiple metrics than MPC- and MARL-based baselines. Under randomly fluctuating demand, it also significantly outperforms pure MARL in generalization, demonstrating strong potential for industrial deployment.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Optimization and Control (math.OC) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.35152 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:14:28.904Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).