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

Sheyda Saneinejad · Matthew J. Roorda

29 citations0/0 files storedTransportation Letters

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Data Management and Algorithms

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.

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References

1995Sequence Analysis: New Methods for Old Ideas 2003A learning-based transportation oriented simulation system 2003Prototype Model of Household Activity-Travel Scheduling 1986Repetition and Variability in Urban Travel 2002Activity pattern similarity: a multidimensional sequence alignment method 1983A Flexible and Integrated Methodology for Analytical Classification of Daily Travel-Activity Behavior 2001A Position-Sensitive Sequence-Alignment Method Illustrated for Space–Time Activity-Diary Data 2001Pattern Recognition in Complex Activity Travel Patterns: Comparison of Euclidean Distance, Signal-Processing Theoretical, and Multidimensional Sequence Alignment Methods

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2021Visualizing, clustering, and characterizing activity-trip sequences via weighted sequence alignment and functional data analysis 2020Exploration of Statewide Fragmentation of Activity and Travel and a Taxonomy of Daily Time Use Patterns using Sequence Analysis in California 2018Are Activity Patterns Stable or Variable? Analysis of Three-Year Panel Data 2022A critical review of activity participation decision: a key component of activity-based travel demand models

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