  In this study, we present a machine learning approach to infer the worker and
student mobility flows on daily basis from static censuses. The rapid
urbanization has made the estimation of the human mobility flows a critical
task for transportation and urban planners. The primary objective of this paper
is to complete individuals' census data with working and studying trips,
allowing its merging with other mobility data to better estimate the complete
origin-destination matrices. Worker and student mobility flows are among the
most weekly regular displacements and consequently generate road congestion
problems. Estimating their round-trips eases the decision-making processes for
local authorities. Worker and student censuses often contain home location,
work places and educational institutions. We thus propose a neural network
model that learns the temporal distribution of displacements from other
mobility sources and tries to predict them on new censuses data. The inclusion
of multi-task learning in our neural network results in a significant error
rate control in comparison to single task learning.
