A Bioinspired Sequence Alignment Approach for Modeling Weekly Activity Patterns

Abstract

This research proposes a multi-module framework to derive weekly representative travel patterns from single-day travel diaries. It employs hierarchical clustering to group samples with homogeneous activity patterns, followed by progressive multiple sequence alignment to construct day-level representative patterns. These day-level patterns are then adjoined based on similarity to obtain week-level representative activity patterns, ultimately producing archetypal weekly pseudo-diaries. The analysis of weekly activity patterns based on the formulated longitudinal data revealed diverse insights into weekly travel behaviours. Results show that a distinct difference exists in weekly activity patterns and time-use behaviour across population groups. For working groups, shorter work durations on Fridays were observed, and the weekly work duration for teleworkers was found to be lower than that of workplace workers. The proposed framework represents a significant step towards multi-day activity-based travel demand modelling, especially in the face of data limitations.