Characterizing activity sequences using profile Hidden Markov Models. Issue 13 (1st August 2015)

• Record Type: Journal Article
• Authors: Liu, Feng; Janssens, Davy; Cui, JianXun; Wets, Geert; Cools, Mario
• Is Part Of: Expert systems with applications. Volume 42:Issue 13(2015)
• Page Start: 5705
• Page End: 5722
• Publication Date: 2015-08-01
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.02.057
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st165">Highlights</title> <sec> <p id="sp0005"> <list id="l0005"> <list-item id="o0005"> <label></label> <p id="p0450">Activity sequences extracted from travel diaries are characterized by pHMMs.</p> </list-item> <list-item id="o0010"> <label></label> <p id="p0455">The models quantify the probabilities of activities and their sequential order.</p> </list-item> <list-item id="o0015"> <label></label> <p id="p0460">They can be used for an improved understanding of activity-travel behavior.</p> </list-item> <list-item id="o0020"> <label></label> <p id="p0465">This method can be integrated into activity-based transportation model validation.</p> </list-item> <list-item id="o0025"> <label></label> <p id="p0470">It is widely applicable to analyze a group of any related but short sequences.</p> </list-item> </list> </p> </sec> </abstract>