  Human mobility has a significant impact on several layers of society, from
infrastructural planning and economics to the spread of diseases and crime.
Representing the system as a complex network, in which nodes are assigned to
regions (e.g., a city) and links indicate the flow of people between two of
them, physics-inspired models have been proposed to quantify the number of
people migrating from one city to the other. Despite the advances made by these
models, our ability to predict the number of commuters and reconstruct mobility
networks remains limited. Here, we propose an alternative approach using
machine learning and 22 urban indicators to predict the flow of people and
reconstruct the intercity commuters network. Our results reveal that
predictions based on machine learning algorithms and urban indicators can
reconstruct the commuters network with 90.4% of accuracy and describe 77.6% of
the variance observed in the flow of people between cities. We also identify
essential features to recover the network structure and the urban indicators
mostly related to commuting patterns. As previously reported, distance plays a
significant role in commuting, but other indicators, such as Gross Domestic
Product (GDP) and unemployment rate, are also driven-forces for people to
commute. We believe that our results shed new lights on the modeling of
migration and reinforce the role of urban indicators on commuting patterns.
Also, because link-prediction and network reconstruction are still open
challenges in network science, our results have implications in other areas,
like economics, social sciences, and biology, where node attributes can give us
information about the existence of links connecting entities in the network.

Physics and Society;Machine Learning;Social and Information Networks;Machine Learning