On Predicting Sociodemographics from Mobility Signals

TL;DR

This paper presents a novel approach to predict sociodemographic attributes from mobility data using interpretable features, uncertainty quantification, and multitask learning to improve accuracy and generalization across contexts.

Contribution

It introduces behaviorally grounded mobility descriptors, diagnostic tools for model confidence, and a multitask learning framework for better sociodemographic prediction.

Findings

Higher-order mobility features improve prediction accuracy.

Uncertainty metrics help quantify model confidence.

Multitask learning enhances generalization across datasets.

Abstract

Inferring sociodemographic attributes from mobility data could help transportation planners better leverage passively collected datasets, but this task remains difficult due to weak and inconsistent relationships between mobility patterns and sociodemographic traits, as well as limited generalization across contexts. We address these challenges from three angles. First, to improve predictive accuracy while retaining interpretability, we introduce a behaviorally grounded set of higher-order mobility descriptors based on directed mobility graphs. These features capture structured patterns in trip sequences, travel modes, and social co-travel, and significantly improve prediction of age, gender, income, and household structure over baselines features. Second, we introduce metrics and visual diagnostic tools that encourage evenness between model confidence and accuracy, enabling planners to quantify uncertainty. Third, to improve generalization and sample efficiency, we develop a multitask learning framework that jointly predicts multiple sociodemographic attributes from a shared representation. This approach outperforms single-task models, particularly when training data are limited or when applying models across different time periods (i.e., when the test set distribution differs from the training set).