  Human migration is a type of human mobility, where a trip involves a person
moving with the intention of changing their home location. Predicting human
migration as accurately as possible is important in city planning applications,
international trade, spread of infectious diseases, conservation planning, and
public policy development. Traditional human mobility models, such as gravity
models or the more recent radiation model, predict human mobility flows based
on population and distance features only. These models have been validated on
commuting flows, a different type of human mobility, and are mainly used in
modeling scenarios where large amounts of prior ground truth mobility data are
not available. One downside of these models is that they have a fixed form and
are therefore not able to capture more complicated migration dynamics. We
propose machine learning models that are able to incorporate any number of
exogenous features, to predict origin/destination human migration flows. Our
machine learning models outperform traditional human mobility models on a
variety of evaluation metrics, both in the task of predicting migrations
between US counties as well as international migrations. In general, predictive
machine learning models of human migration will provide a flexible base with
which to model human migration under different what-if conditions, such as
potential sea level rise or population growth scenarios.
