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Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization — arXiv:2610.32698

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.32698
License (as archived): CC0-1.0
Archived: 2026-10-26T02:05:33.355Z
SHA-256 of the archived text: 2c8734ae0b8c37efb6402e352ba518656967afaaf3e62c34ea8e15d0c5a8d435
Stable URL of this page: https://metraipolis.com/documento/dc_a36ff56efb95

How to cite

Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization — arXiv:2610.32698 — arXiv (en), https://arxiv.org/abs/2610.32698 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a36ff56efb95 (Metraipolis archive dc_a36ff56efb95, sha256 2c8734ae0b8c37efb6402e352ba518656967afaaf3e62c34ea8e15d0c5a8d435)

BibTeX

@misc{dca36ff56efb95,
  title = {Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization — arXiv:2610.32698},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.32698},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a36ff56efb95; sha256 2c8734ae0b8c37efb6402e352ba518656967afaaf3e62c34ea8e15d0c5a8d435},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.32698. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Calibrating agent-based models (ABMs) is computationally demanding, as traditional black-box optimization typically requires hundreds or thousands of independent simulation runs. To reduce this burden, we introduce an online calibration framework that estimates parameters within a single simulation run. The approach combines black-box optimization with stochastic batch optimization, a method widely applied in machine learning but not yet explored in ABM calibration. Using MATSim as a case study, we treat each simulation iteration as a stochastic batch, enabling incremental parameter updates as agents make decisions. This online calibration paradigm allows model parameters and agent behaviors to co-evolve, achieving convergence without restarting the simulation. We demonstrate the effectiveness of our method by calibrating utility function parameters in MATSim for a large-scale transportation scenario of Switzerland. Results show that mode shares align closely with observed data within a typical simulation timeframe, confirming the feasibility and efficiency of the approach. By embedding calibration directly into the simulation loop, our method opens new possibilities for real-time, scalable, and efficient calibration of complex ABMs.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.32698 — 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 2026-10-26T02:04:36.282Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).