A time-use activity-pattern recognition model for activity-based travel demand modeling
Learning Daily Activity Sequences of Population Groups using Random Forest Theory
Identification of Representative Patterns of Time Use Activity Through Fuzzy C-Means Clustering
Daily activity and travel sequences of students, faculty and staff at a large Canadian university
Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model