Multi-task learning of daily work and study round-trips from survey data
This work addresses the need for accurate human mobility flow estimation for transportation and urban planners, though it is incremental as it builds on existing methods with multi-task learning.
The study tackled the problem of estimating daily worker and student mobility flows from static census data by developing a neural network model that uses multi-task learning to predict temporal distributions of displacements, resulting in a significant reduction in error rates compared to single-task learning.
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.