用预训练时序模型实现特殊事件人流零样本概率预测，提升决策可靠性。
Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting
- 直接使用预训练时序模型，无需本地数据微调即可预测人流
- 在短时事件中保持高精度，并有效捕捉突发波动与极端风险
- 为应急管理者提供可信赖的预测依据，尤其适用于数据稀缺场景
大型特殊活动期间的人流管理需依赖可靠的实时行人流量预测以保障公共安全与运营效率。然而，由于历史数据稀缺、数据分布异构及事件期内观测窗口短暂，传统监督式预测方法面临挑战。为支持实际决策，预测不仅需准确点估计，还应包含有信息量的不确定性评估。概率不确定性量化在此方面至关重要，尤其能捕捉突发波动与尾部风险。本文探讨了预训练时序基础模型作为轻量级零样本概率预测方法的可行性，无需大量本地再训练。基于面向决策的指标，在SAIL2025事件案例上对两种时序基础模型进行了全面评估，并提炼出可供人流管理者参考的实用洞察，明确零样本预测在何种条件下仍具操作可靠性。
原文摘要 · Abstract (English)
Managing massive crowds during infrequent special events requires reliable real-time pedestrian-flow forecasting to ensure public safety and operational efficiency. However, supervised forecasting methods face limitations in these contexts due to scarce historical data, heterogeneous data distributions, and short in-event observation windows. To effectively support operational decision-making, forecasts should provide not only accurate point estimates but also informative predictive uncertainty. Probabilistic uncertainty quantification plays a critical role in this aspect, particularly capturing sudden volatility and tail risks. This paper investigates pretrained time series foundation models as a lightweight approach for zero-shot probabilistic forecasting without extensive local retraining. Using decision-oriented metrics tailored to short events, we conduct a comprehensive assessment of two time series foundation models on crowd forecasting, with the SAIL2025 event as a use case. We then distill practical insights for crowd managers, specifying when zero-shot forecasts remain operationally reliable.
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