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

Abstract

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.

Spatial Characteristics and Influencing Factors of Commuting in Central Urban Areas Using Mobile Phone Data: A Case Study of Nanning

“…where L in Equation ( 3) is the stratification of the independent or dependent variable, i.e., classification and partitioning, N h and N represent the number of cells in stratum h and the whole region, respectively and σ h and σ 2 h denote the variance of the dependent variable in stratum h and the whole region, respectively. Furthermore, SSW and SST state the sum of the variance within the stratum and the total variance of the whole region, respectively, which were calculated as shown in Equations ( 4) and (5). The q values were in the range of (0,1).…”

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