将聚合出行统计数据提炼为语言模型策略以用于事件后人群模拟
Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation
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中文总结 AI 辅助
该研究针对仅能获取聚合出行统计数据的场景，通过微调语言模型人群智能体并结合迭代比例拟合与低秩适配器，降低了事件后人群模拟的目的地份额误差。
中文摘要 AI 辅助
行人模拟器需要为每个智能体设置行为规则，但隐私通常限制了用于设置规则的数据，仅能获取区域级设备数量和起终点（OD）流量等聚合统计数据，不存在个体轨迹。这类聚合数据无法确定个体行为，因为多组不同决策都能产生相同的计数结果。我们微调了一个语言模型人群智能体，使模拟人群匹配观测到的目的地构成，即出发人群前往各兴趣点的比例。我们从OD流量中读取该目标，并通过迭代比例拟合将模型自身的目的地分布重新加权至该目标。由于微调会放大主导目的地类别，我们将低秩适配器适配到经重新采样的轨迹上，该轨迹在放大后能达到目标的修正训练构成。在两场棒球比赛的移动网络计数数据上，微调后的智能体无需推理时修正即可运行，将目的地份额误差降低了25%，且各策略间的网格相关性保持相似。
英文摘要
Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, namely zone-level device counts and origin-to-destination (OD) flows, with no individual trajectories. Such aggregates under-determine individual behaviour, because many different sets of decisions reproduce the same counts. We fine-tune a language model crowd agent so that the simulated population matches the observed destination composition, the fraction of the departing crowd heading to each point of interest. We read this target from the OD flow and reweight the model's own destination distribution onto it by iterative proportional fitting. Because fine-tuning inflates the dominant destination class, we fit the low-rank adapter to trajectories resampled to a corrected training composition that reaches the target after this inflation. On mobile network counts from two baseball games the fine-tuned agent runs without inference-time correction, cutting the destination-share error by 25%, while the grid correlation remains similar across policies.
发表机构
- The University of Osaka（大阪大学）
- RIKEN Center for Computational Science（理化学研究所计算科学中心）
机构由 AI 辅助整理，请以论文原文为准。
