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AgenticMobility: An Open-Source Platform for Multi-AI-Agent Mobility Dynamics — arXiv:2611.01918

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01918
License (as archived): CC0-1.0
Archived: 2026-11-03T10:49:22.970Z
SHA-256 of the archived text: dddf3bc4d634d27aa43b7bae5649628e776bc42616d5a0aef4276be32d6ff24f
Stable URL of this page: https://metraipolis.com/documento/dc_7fc09bfeb545

How to cite

AgenticMobility: An Open-Source Platform for Multi-AI-Agent Mobility Dynamics — arXiv:2611.01918 — arXiv (en), https://arxiv.org/abs/2611.01918 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7fc09bfeb545 (Metraipolis archive dc_7fc09bfeb545, sha256 dddf3bc4d634d27aa43b7bae5649628e776bc42616d5a0aef4276be32d6ff24f)

BibTeX

@misc{dc7fc09bfeb545,
  title = {AgenticMobility: An Open-Source Platform for Multi-AI-Agent Mobility Dynamics — arXiv:2611.01918},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01918},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7fc09bfeb545; sha256 dddf3bc4d634d27aa43b7bae5649628e776bc42616d5a0aef4276be32d6ff24f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01918. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding mobility dynamics and the effects of urban transportation management policies requires examining how individual travel decisions, traffic conditions, and policy interventions influence one another over time. We present AgenticMobility, an open-source experimental platform for studying how mobility AI agents and other AI agents interact, adapt through repeated experience, and collectively shape mobility outcomes. Persistent agents retain identities and relevant states across rounds, with explicit interfaces defining information access, admissible actions, and decision schedules; linked records trace decisions, execution, and feedback. In the current implementation, large language model (LLM) agents act as travelers choosing routes and departure times and as a manager periodically adjusting link charges within MATSim. A preliminary demonstration spans 100 simulated days in each of five configurations, with one run per configuration and a shared simulation seed. In the joint traveler--manager run, all 1,179,300 finalized target decisions satisfied action constraints; execution checks verified implementation of accepted choices and preservation of personal memories across days. Trajectories show stable route choices alongside departure-time adjustments and differences between controlled-trip and population-wide outcomes. The platform supports experiments on individual adaptation, collective outcomes, and management interventions by varying information access, retained experience, and management feedback. Source code is available at this https URL

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI); Physics and Society (physics.soc-ph).
Pagina del lavoro: https://arxiv.org/abs/2611.01918 — 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 2026-11-03T10:49:22.866Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).