Passive location tracking through smartphone-based services offers promising avenues for mobility research, yet its reliability and analytical value remain underexplored. This study presents a comprehensive methodology for extracting commuting patterns from Google Maps Timeline (GMT) data and applies it to a 30-day longitudinal dataset on 294 participants, collected in 2024 in Southern Switzerland. We developed and released an open-source data processing methodology to convert raw GMT activity segments into discrete location bins, enabling the detection of significant places (home and workplace) and commuting trips through unsupervised algorithms. Our findings show that GMT achieves a mean location coverage of 72% across study participants and days with a 200-meter distance threshold between consecutive segments, and allows for the reliable identification of commuting trips in days in which study participants self-reported having worked. While GMT presents limitations in data completeness and algorithm transparency, it offers scalable, low-burden alternatives to traditional mobility data collection methods. Future research should explore its applicability across diverse populations and contexts, and consider open-source alternatives for enhanced transparency and control. • Google Maps Timeline (GMT) provides a passive solution for location tracking. • We provide methods to process raw GMT data into significant places and commuting trips. • We test our methods on real-world data from 294 people in Southern Switzerland. • We find a GMT coverage of 72%, and reliable home and workplaces for 65% of study participants. • GMT is a low-burden tool complementing self-reports for policy making and large-scale mobility research.
Marzorati et al. (Tue,) studied this question.