Revealing temporal stay patterns in human mobility using large-scale mobile phone location data

The emergence of large-scale human spatiotemporal tracing data inspired researchers to investigate human mobility patterns from different perspectives with potential implications for urban planning, traffic management, and emergency response. Previous studies investigated the mechanism of human mobility by constructing spatial mobility motifs based on mobile phone datasets, but ignored their temporal characteristics. To compensate for this, this study extracted temporal stay patterns based on spatial mobility motifs. Using mobile phone location data from Shenzhen, China, we identified stop location sequences for users and constructed home-based mobility motifs according to predefined rules, and observed that approximately 97% of users can be characterized by the 10 identified home-based mobility motifs. We designed a method to quantify the similarity of temporal stay characteristics, and the primary temporal stay patterns were extracted by classifying users with similar stay rhythms. Our results enrich the knowledge of human travel behavior from both spatial and temporal perspectives.

Automated summary

This study explores human mobility patterns from both spatial and temporal perspectives using mobile phone location data and spatial mobility motifs. By extracting temporal stay patterns and defining rules, the primary temporal stay patterns of Shenzhen users were successfully identified.