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Prediction is Better than Detection: Traffic Congestion Control using Drones — arXiv:2609.39637

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39637
License (as archived): CC0-1.0
Archived: 2026-10-07T12:39:43.619Z
SHA-256 of the archived text: 0f6319de9343a2a6868f65d33f6f6b9e5e5df3daa482bd1702afcd34d232e439
Stable URL of this page: https://metraipolis.com/documento/dc_ea46c8dba2ab

How to cite

Prediction is Better than Detection: Traffic Congestion Control using Drones — arXiv:2609.39637 — arXiv (en), https://arxiv.org/abs/2609.39637 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea46c8dba2ab (Metraipolis archive dc_ea46c8dba2ab, sha256 0f6319de9343a2a6868f65d33f6f6b9e5e5df3daa482bd1702afcd34d232e439)

BibTeX

@misc{dcea46c8dba2ab,
  title = {Prediction is Better than Detection: Traffic Congestion Control using Drones — arXiv:2609.39637},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39637},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea46c8dba2ab; sha256 0f6319de9343a2a6868f65d33f6f6b9e5e5df3daa482bd1702afcd34d232e439},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39637. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scaling holds once sensing drives downstream action rather than mere observation. We study this question for a team of drones performing traffic-jam detection and prediction in a simulated road network, whose reports drive an adaptive traffic-signal controller in closed loop. We build a multi-agent simulation, with vehicles following Nagel-Schreckenberg cellular-automaton dynamics and drones patrolling junctions via a round-robin policy, and sweep fleet size, traffic level, and network size to evaluate detection rate, detection delay, and prediction rate. We show how performance plateaus for fleet size approximating the number of junctions being monitored, and offer a general fleet-provisioning rule for persistent-monitoring deployments. More significantly, adapting the signal on a predicted jam, rather than a detected one, roughly doubles the resulting reduction in jam duration, showing that the value of onboard prediction in a sensing-to-action pipeline can exceed the value of adding more robots. Prediction accuracy, not sensing coverage, is now the binding constraint on further improvement, pointing to onboard inference, not fleet size, as the more promising direction for future work.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.MA).
Categorie arXiv: Robotics (cs.RO) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2609.39637 — 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 2026-10-07T12:12:18.659Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).