迈向可靠的零样本人群预测：评估用于特殊活动行人预测的时间序列基础模型
Towards Reliable Zero-Shot Crowd Forecasting: Evaluating Time Series Foundation Models for Special Event Pedestrian Forecasting
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中文总结 AI 辅助
研究特殊活动行人预测问题，采用预训练时间序列基础模型进行零样本概率预测，以SAIL2025事件为案例评估两个模型，为人群管理者明确零样本预测可靠的时机，助力特殊活动人群管理的运营决策。
中文摘要 AI 辅助
在不频繁的特殊活动期间管理大量人群需要可靠的实时行人流量预测，以确保公共安全和运营效率。然而，由于历史数据稀缺、数据分布异质以及事件期间观察窗口短，监督预测方法在这些情况下存在局限性。为有效支持运营决策，预测不仅应提供准确的点估计，还应提供信息丰富的预测不确定性。概率不确定性量化在这方面起着关键作用，特别是捕捉突然的波动性和尾部风险。本文研究预训练的时间序列基础模型，作为一种无需大量本地再训练的轻量级零样本概率预测方法。使用针对短事件量身定制的面向决策的指标，我们以SAIL2025事件为案例，对两个时间序列基础模型在人群预测方面进行了全面评估。然后，我们为人群管理者提炼了实用见解，明确了零样本预测何时在操作上仍然可靠。
英文摘要
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.
发表机构
- Delft University of Technology (TU Delft)（代尔夫特理工大学）
- German Aerospace Center (DLR)（德国航空航天中心）
机构由 AI 辅助整理，请以论文原文为准。
