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

TL;DR

This paper introduces a neural network-based multi-task learning approach to infer daily worker and student mobility flows from static census data, aiding urban planning and congestion management.

Contribution

It presents a novel multi-task neural network model that improves the estimation of daily mobility round-trips from census data, integrating multiple mobility sources.

Findings

Multi-task learning reduces prediction error compared to single-task models.

The model effectively estimates daily work and study trips from static census data.

Enhanced mobility flow estimation supports better urban transportation planning.

Abstract

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.