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- W647470691 abstract "Extension of the 24-hour activity models to weeklong models and generating better routine activity skeletons, which are later filled in with non-routine activity episodes are identified as two areas of improvement in current activity-based modeling techniques. This paper utilizes multidimensional sequence alignment methods to measure similarities between routine weekly activity sequences of 282 surveyed individuals, as reported in a specialized survey of routine weekly schedules conducted in Toronto, Canada. Similar activity patterns are classified into a number of clusters. General behavioral patterns of the resulting clusters are described and analyzed based on socioeconomic activities of members of each cluster. Significant differences are found in a variety of socioeconomic variables that describe individual membership in each cluster, including age, income, gender, employment status, student status, marital status, drivers license, cell phone usage and education level." @default.
- W647470691 created "2016-06-24" @default.
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- W647470691 date "2008-01-01" @default.
- W647470691 modified "2023-09-23" @default.
- W647470691 title "Application of Sequence Alignment Methods in Clustering and Analysis of Routine Weekly Activity Schedules" @default.
- W647470691 hasPublicationYear "2008" @default.
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