Abstract | ||
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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. |
Year | DOI | Venue |
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2012 | https://doi.org/10.1007/s10618-012-0264-z | Data Mining and Knowledge Discovery |
Keywords | DocType | Volume |
Human activity,Eigen decomposition,Daily activity clustering,Metropolitan area,Statistical learning | Journal | 25 |
Issue | ISSN | Citations |
3 | 1384-5810 | 53 |
PageRank | References | Authors |
2.52 | 17 | 3 |
Name | Order | Citations | PageRank |
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Shan Jiang | 1 | 97 | 4.62 |
Joseph Ferreira | 2 | 146 | 8.72 |
Marta C. González | 3 | 299 | 18.26 |