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SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-faced Adaptive Traffic Control for Reliable Intelligent Transportation Systems — arXiv:2608.18263

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18263
License (as archived): CC0-1.0
Archived: 2026-10-18T13:53:11.701Z
SHA-256 of the archived text: 9c4e2e1c5d07829f92ead4d53c803b4dc6c7e0adc13235da3326acc71c6ab291
Stable URL of this page: https://metraipolis.com/documento/dc_406fcbf409d4

How to cite

SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-faced Adaptive Traffic Control for Reliable Intelligent Transportation Systems — arXiv:2608.18263 — arXiv (en), https://arxiv.org/abs/2608.18263 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_406fcbf409d4 (Metraipolis archive dc_406fcbf409d4, sha256 9c4e2e1c5d07829f92ead4d53c803b4dc6c7e0adc13235da3326acc71c6ab291)

BibTeX

@misc{dc406fcbf409d4,
  title = {SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-faced Adaptive Traffic Control for Reliable Intelligent Transportation Systems — arXiv:2608.18263},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18263},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_406fcbf409d4; sha256 9c4e2e1c5d07829f92ead4d53c803b4dc6c7e0adc13235da3326acc71c6ab291},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18263. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Traffic signal control is a challenging sequential decision-making problem requiring a balance between throughput, vehicle delay fairness, signal stability, predictable switching, and emergency vehicle prioritisation. Existing reinforcement learning (RL) approaches often rely on predefined objectives and offer limited adaptability to changing operational priorities. They may also generalise poorly across geometrically equivalent intersections. To address these limitations, SIGMA (Symmetry-aware, Intelligent, Geometric, Multi-faced Adaptive traffic control) is proposed as a multi-faced RL framework augmented with a large language model (LLM) for dynamic objective adaptation and orientation-invariant policy learning. Natural language emergency instructions are translated into priority vectors that guide a multi-faced actor-critic controller. Symmetry-aware rotational augmentation supports transfer across homogeneous four-way intersections, while an offline-to-online learning strategy enables stable initialisation and adaptation to evolving traffic conditions. Structural properties concerning emergency service levels, graceful degradation under LLM failure, and sensitivity to traffic demand perturbations are established, alongside bootstrap-based statistical validation. Experiments in SUMO across four urban intersections modelled on Kolkata traffic scenarios compare SIGMA with fixed-time, actuated, and Deep Q-Network controllers. Reductions in average waiting times, emergency waiting times, and queue lengths are observed, alongside improved traffic throughput. Empirical reliability and ablation analyses further assess robustness to component failures and geometric orientation. SIGMA integrates language-guided objective adaptation, multi-faced policy learning, and statistical reliability assessment into an adaptive framework for intelligent traffic control.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2608.18263 — 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 2026-10-18T13:50:58.897Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).