Application of sequence alignment methods in clustering and analysis of routine weekly activity schedules

Abstract

Extension of 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 multiple 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 nine clusters. General behavioral patterns of the resulting clusters are described and analyzed based on socioeconomic attributes 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.

Keywords:

• Activity scheduling
• sequence alignment method
• skeleton schedule
• panel survey