Data Mining and Knowledge Discovery

, Volume 25, Issue 3, pp 478-510

First online:

Clustering daily patterns of human activities in the city

  • Shan JiangAffiliated withDepartment of Urban Studies and Planning, Massachusetts Institute of Technology
  • , Joseph FerreiraAffiliated withDepartment of Urban Studies and Planning, Massachusetts Institute of Technology
  • , Marta C. GonzálezAffiliated withDepartment of Civil and Environmental Engineering and Engineering Systems Division, Massachusetts Institute of Technology Email author 

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Data mining and statistical learning techniques are powerful analysis tools yet to be incorporated in the domain of urban studies and transportation research. In this work, we analyze an activity-based travel survey conducted in the Chicago metropolitan area over a demographic representative sample of its population. Detailed data on activities by time of day were collected from more than 30,000 individuals (and 10,552 households) who participated in a 1-day or 2-day survey implemented from January 2007 to February 2008. We examine this large-scale data in order to explore three critical issues: (1) the inherent daily activity structure of individuals in a metropolitan area, (2) the variation of individual daily activities—how they grow and fade over time, and (3) clusters of individual behaviors and the revelation of their related socio-demographic information. We find that the population can be clustered into 8 and 7 representative groups according to their activities during weekdays and weekends, respectively. Our results enrich the traditional divisions consisting of only three groups (workers, students and non-workers) and provide clusters based on activities of different time of day. The generated clusters combined with social demographic information provide a new perspective for urban and transportation planning as well as for emergency response and spreading dynamics, by addressing when, where, and how individuals interact with places in metropolitan areas.


Human activity Eigen decomposition Daily activity clustering Metropolitan area Statistical learning