Journal Title
Title of Journal: Data Min Knowl Disc
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Abbravation: Data Mining and Knowledge Discovery
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Authors: Shan Jiang Joseph Ferreira Marta C González
Publish Date: 2012/04/20
Volume: 25, Issue: 3, Pages: 478-510
Abstract
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 activitybased 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 30000 individuals and 10552 households who participated in a 1day or 2day survey implemented from January 2007 to February 2008 We examine this largescale 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 sociodemographic 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 nonworkers 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
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