“Modeling human mobility is critical to address questions in urban planning, sustainability, public health, and economic development. … At one end of the modeling spectrum we have gravity models, which are easy to interpret but provide modestly accurate predictions of flows. At the other end, we have machine learning models, … which predict mobility more accurately than gravity models but do not provide clear insights on human behavior. Here, we show that simple machine-learned, closed-form models of mobility can predict mobility flows as accurately as complex machine learning models, and extrapolate better.”

Find the paper and list of authors in Nature Communications.