  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.
