Characterizing activity sequences using profile Hidden Markov Models

作者：

Highlights：

• Activity sequences extracted from travel diaries are characterized by pHMMs.

• The models quantify the probabilities of activities and their sequential order.

• They can be used for an improved understanding of activity-travel behavior.

• This method can be integrated into activity-based transportation model validation.

• It is widely applicable to analyze a group of any related but short sequences.

摘要

•Activity sequences extracted from travel diaries are characterized by pHMMs.•The models quantify the probabilities of activities and their sequential order.•They can be used for an improved understanding of activity-travel behavior.•This method can be integrated into activity-based transportation model validation.•It is widely applicable to analyze a group of any related but short sequences.

论文关键词：Profile Hidden Markov Models (pHMMs),Sequence Alignment Methods (SAM),Multiple sequence alignments,Activity sequences,Activity-travel diaries,Mobile phone data

论文评审过程：Available online 12 March 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.02.057