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

Source: Metraipolis archive
Original: https://arxiv.org/abs/2610.32698
License (as archived): CC-BY-4.0
Archived: 2026-10-26T09:12:21.287Z
SHA-256 of the archived text: 7b162ea6a80e7d10328b105eb1c41b304efbaeb6dfe9bd015737090fc01c914c
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How to cite

arXiv 2610.32698: Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization — Metraipolis archive, https://arxiv.org/abs/2610.32698 (CC-BY-4.0); archived copy: https://metraipolis.com/documento/dc_9efc100bbea9 (Metraipolis archive dc_9efc100bbea9, sha256 7b162ea6a80e7d10328b105eb1c41b304efbaeb6dfe9bd015737090fc01c914c)

BibTeX

@misc{dc9efc100bbea9,
  title = {arXiv 2610.32698: Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization},
  howpublished = {Metraipolis archive},
  url = {https://arxiv.org/abs/2610.32698},
  note = {License CC-BY-4.0; archived copy https://metraipolis.com/documento/dc_9efc100bbea9; sha256 7b162ea6a80e7d10328b105eb1c41b304efbaeb6dfe9bd015737090fc01c914c},
  year = {2026}
}

Archived text

Reproduced under CC-BY-4.0. Attribution: Metraipolis archive, 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.

Articolo: «Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization» — Dib, Abdelkader; Balac, Milos (2026-10-23), arXiv:2610.32698, categoria cs.MA (sistemi multi-agente).
Fonte: https://arxiv.org/abs/2610.32698
Licenza dell'articolo indicata su arXiv: CC-BY-4.0 — https://creativecommons.org/licenses/by/4.0. Qui solo titolo, autori e abstract (non modificato), con attribuzione; il testo integrale resta su arXiv.
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