English(EN) Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization

新的在线方法简化了基于智能体的模型校准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 14:23

研究人员开发了一种新颖的在线校准框架，用于通常计算量很大的基于智能体的模型（ABM）。这种新方法将黑盒优化与随机批量优化相结合，后者是一种以前在ABM校准中未充分利用的技术。通过将每次模拟迭代视为一个随机批次，该框架允许在单次模拟运行中进行增量参数更新以及模型参数和智能体行为的共同演化。该方法在瑞士的MATSim交通模型上进行了测试，成功地使出行方式份额与观测数据保持一致，证明了其效率和实时ABM校准的潜力。 AI

影响这种新的校准方法可以加速复杂基于智能体的模型在交通和城市规划等各个领域的开发和部署。

排序理由学术论文，详细介绍了一种用于基于智能体模型校准的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.MA (Multiagent) 阅读 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Milos Balac · 2026-10-23 14:23

基于智能体的交通模型在线随机批量优化校准

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 …

本文如何被排名

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基于智能体的交通模型在线随机批量优化校准

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