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In this study, we present a machine learning approach to infer the worker andstudent mobility flows on daily basis from static censuses. The rapidurbanization has made the estimation of the human mobility flows a criticaltask for transportation and urban planners. The primary objective of this paperis to complete individuals' census data with working and studying trips,allowing its merging with other mobility data to better estimate the completeorigin-destination matrices. Worker and student mobility flows are among themost weekly regular displacements and consequently generate road congestionproblems. Estimating their round-trips eases the decision-making processes forlocal authorities. Worker and student censuses often contain home location,work places and educational institutions. We thus propose a neural networkmodel that learns the temporal distribution of displacements from othermobility sources and tries to predict them on new censuses data. The inclusionof multi-task learning in our neural network results in a significant errorrate control in comparison to single task learning.

Multi-task learning of daily work and study round-trips from survey data