Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

Abstract

The potential of passively generated big data sources in transport modelling is well-recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground-truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent-based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation-based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.