Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers

TL;DR

This paper introduces a transformer-based generative model that augments mobility data for underrepresented shift workers, enabling more inclusive urban transportation planning by transforming fragmented GPS data into complete activity patterns.

Contribution

The paper presents a novel transformer model with period-aware embeddings and a transition-focused loss to generate realistic activity patterns for shift workers from incomplete GPS data.

Findings

Achieves distributional alignment with GPS data (JSD < 0.02).

Effectively captures unique activity rhythms of shift workers.

Provides a data augmentation tool for inclusive transportation planning.

Abstract

This paper addresses a critical gap in urban mobility modeling by focusing on shift workers, a population segment comprising 15-20% of the workforce in industrialized societies yet systematically underrepresented in traditional transportation surveys and planning. This underrepresentation is revealed in this study by a comparative analysis of GPS and survey data, highlighting stark differences between the bimodal temporal patterns of shift workers and the conventional 9-to-5 schedules recorded in surveys. To address this bias, we introduce a novel transformer-based approach that leverages fragmented GPS trajectory data to generate complete, behaviorally valid activity patterns for individuals working non-standard hours. Our method employs periodaware temporal embeddings and a transition-focused loss function specifically designed to capture the unique activity rhythms of shift workers and mitigate the inherent biases in conventional transportation datasets. Evaluation shows that the generated data achieves remarkable distributional alignment with GPS data from Los Angeles County (Average JSD < 0.02 for all evaluation metrics). By transforming incomplete GPS traces into complete, representative activity patterns, our approach provides transportation planners with a powerful data augmentation tool to fill critical gaps in understanding the 24/7 mobility needs of urban populations, enabling precise and inclusive transportation planning.