Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires

Abstract

Natural hazards, such as wildfires, pose a significant threat to communities worldwide. 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. To tackle this research gap, the study develops a new methodological framework for modeling highly granular spatiotemporal trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. 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 using 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 weekend indicator, population change, evacuation order/warning information, and proximity to fire, which are consistent with behavioral theories and empirical findings. SA-MGCRN can be directly used in future wildfire events to assist real-time decision-making and emergency management.