  Modeling of human mobility is critical to address questions in urban planning
and transportation, as well as global challenges in sustainability, public
health, and economic development. However, our understanding and ability to
model mobility flows within and between urban areas are still incomplete. At
one end of the modeling spectrum we have simple so-called gravity models, which
are easy to interpret and provide modestly accurate predictions of mobility
flows. At the other end, we have complex machine learning and deep learning
models, with tens of features and thousands of parameters, which predict
mobility more accurately than gravity models at the cost of not being
interpretable and not providing insight on human behavior. Here, we show that
simple machine-learned, closed-form models of mobility are able to predict
mobility flows more accurately, overall, than either gravity or complex machine
and deep learning models. At the same time, these models are simple and
gravity-like, and can be interpreted in terms similar to standard gravity
models. Furthermore, these models work for different datasets and at different
scales, suggesting that they may capture the fundamental universal features of
human mobility.
