Real-time forecasting of travel demand during wildfire evacuations is crucial
for emergency managers and transportation planners to make timely and
better-informed decisions. However, few studies focus on accurate travel demand
forecasting in large-scale emergency evacuations. Therefore, this study
develops and tests a new methodological framework for modeling trip generation
in wildfire evacuations by using (a) large-scale GPS data generated by mobile
devices and (b) state-of-the-art AI technologies. The proposed methodology aims
at forecasting evacuation trips and other types of trips. Based on the travel
demand inferred from the GPS data, we develop a new deep learning model, i.e.,
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along
with a model updating scheme to achieve real-time forecasting of travel demand
during wildfire evacuations. The proposed methodological framework is tested in
this study for a real-world case study: the 2019 Kincade Fire in Sonoma County,
CA. The results show that SA-MGCRN significantly outperforms all the selected
state-of-the-art benchmarks in terms of prediction performance. Our finding
suggests that the most important model components of SA-MGCRN are evacuation
order/warning information, proximity to fire, and population change, which are
consistent with behavioral theories and empirical findings.

使用 GPS 数据和深度学习技术，本研究提出了一种新的方法框架，旨在对野火疏散中的需求进行实时预测，并在实际案例中证明了此方法在预测性能方面显著优于当前流行的方法。

面向山火情境的多图卷积循环网络 (SA-MGCRN) 用于旅行需求预测

Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN)  for Travel Demand Forecasting During Wildfires

In real-time forecasting in public health, data collection is a non-trivial
and demanding task. Often after initially released, it undergoes several
revisions later (maybe due to human or technical constraints) - as a result, it
may take weeks until the data reaches to a stable value. This so-called
'backfill' phenomenon and its effect on model performance has been barely
studied in the prior literature. In this paper, we introduce the multi-variate
backfill problem using COVID-19 as the motivating example. We construct a
detailed dataset composed of relevant signals over the past year of the
pandemic. We then systematically characterize several patterns in backfill
dynamics and leverage our observations for formulating a novel problem and
neural framework Back2Future that aims to refines a given model's predictions
in real-time. Our extensive experiments demonstrate that our method refines the
performance of top models for COVID-19 forecasting, in contrast to non-trivial
baselines, yielding 18% improvement over baselines, enabling us obtain a new
SOTA performance. In addition, we show that our model improves model evaluation
too; hence policy-makers can better understand the true accuracy of forecasting
models in real-time.

本文介绍了一种基于 COVID-19 疫情数据的多元回溯问题，构建了一个详细的数据集，并提出了一个名为 Back2Future 的神经框架，能够在实时中精确预测并改善现有的模型性能，实验表明，与基准线相比，该方法产生 18% 的改进，进而提高模型评价的准确性。