基于在线随机批优化的智能体交通模型校准
原标题：Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization
AI 摘要
该论文提出一种在线校准框架，将黑盒优化与随机批优化结合，把校准过程嵌入单次仿真运行中，而非传统需要数百上千次独立仿真的黑盒优化循环。作者以MATSim为案例，将每次仿真迭代视为一个随机批次，使模型参数与智能体行为共同演化并收敛。在瑞士大规模交通场景中校准效用函数参数后，模拟出行方式分担率在典型仿真时间内与观测数据高度吻合，验证了该方法的可行性与效率。
正文节选
Agent-Based Transport Model Calibration via Online Stochastic Batch Optimization Note: Presented at the World Conference on Transport Research (WCTR 2026), Toulouse, France. Abstract 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