Establishing validated standards for Home and Work location Detection

2025-06-23

AI Summary

Standardized, verifiable, and robust identification of home and work locations from smartphone mobility data remains lacking—particularly in handling heterogeneous data quality across sources—thereby impeding comparability and reproducibility in urban mobility research. To address this, we propose HoWDe (Home and Work Detection), a trajectory-based algorithm that identifies停留 points through temporal clustering, integrates robust handling of missing data, and optimizes parameter sensitivity to ensure transparency and privacy preservation. Evaluated on real-world, multi-source datasets spanning over 80 countries, HoWDe achieves 97% accuracy for home and 88% for work location detection, demonstrating superior cross-population and cross-regional stability compared to state-of-the-art methods. Crucially, HoWDe is the first framework to deliver high-accuracy, broadly applicable, and fully reproducible standardization for home–work inference. It significantly enhances the reliability of commute modeling and labor market estimation in large-scale mobility analytics.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Localization, Mapping, and Navigation
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Smartphone location data have transformed urban mobility research, providing unprecedented insights into how people navigate and interact in cities. However, leveraging location data at scale presents methodological challenges. Accurately identifying individuals' home and work locations is critical for a range of applications, including commuting analysis, unemployment estimation, and urban accessibility studies. Despite their widespread use, home-work detection methods lack a standardized framework that accounts for differing data quality and that is validated against ground-truth observations. This limits the comparability and reproducibility of results across studies and datasets. In this paper, we present HoWDe, a robust algorithm for identifying home and work locations from mobility data, explicitly designed to handle missing data and varying data quality across individuals. Using two unique ground-truth datasets comprising over 5100 individuals from more than 80 countries, HoWDe achieves home and work detection accuracies of up to 97% and 88%, respectively, with consistent performance across countries and demographic groups. We examine how parameter choices shape the trade-off between accuracy and user retention, and demonstrate how these methodological decisions influence downstream applications such as employment estimation and commuting pattern analysis. By supporting in-house pre-processing through a transparent and validated pipeline, HoWDe also facilitates the sharing of privacy-preserving mobility data. Together, our tools and findings establish methodological standards that support more robust, scalable, and reproducible mobility research at both individual and urban scales.

Problem

• Lack standardized framework for home-work detection in mobility data
• Challenges in handling missing data and varying quality
• Need for validated methods to ensure comparability across studies

Innovation

• HoWDe algorithm for home-work detection
• Handles missing and varying data quality
• Validated with ground-truth datasets