Machine learning for human mobility during disasters: A systematic literature review

Abstract

Understanding and predicting human mobility during disasters is crucial for effective disaster management. Knowledge about population locations can greatly enhance rescue missions and evacuations. Realistic models that reflect observable mobility patterns and volumes are crucial for estimating population locations. However, existing models are limited in their applicability to disasters, as they are typically restricted to describing regular mobility patterns. Machine learning models trained to capture patterns observable in provided training data also face this limitation. The necessity of large amounts of training data for machine learning models, coupled with the scarcity of data on mobility in disasters, often constrains the feasibility of their training. Various strategies have been developed to overcome this issue, which we present and discuss in this systematic literature review. Our review aims to support and accelerate the synthesis of novel approaches by establishing a knowledge base for future research. This review identified a condensed field of related contributions exhibiting high methodology and context diversity. We classified and analyzed the relevant contributions based on their proposed approach and subsequently discussed and compared them qualitatively. Finally, we elaborated on general challenges and highlighted areas for future research.