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Understanding the travel behavior of individuals grouped by similar time-use activity patterns can contribute greatly to modeling regional spatial and temporal patterns of transport demand. In this paper, we present a comprehensive modeling framework to forecast and replicate individuals’ travel behavior, labeled as the Scheduler for Activities, Locations, and Travel (SALT). The prototype version of the SALT framework comprises a series of modules that employ behaviorally-based econometric, machine-learning, and data-mining techniques. The SALT model is cross-validated with 30% of the out-of-home sample survey data from the large Halifax Space Time Activity Research (STAR) household survey. Results show that the SALT scheduling model is able to assemble the travelers’ 24-hour schedules with an average 82% accuracy compared to the observed data. The proposed simulation modeling framework is useful for deeper understanding of individuals’ activity-travel decisions and may be utilized to examine sensitive policy issues such as transportation control measures and congestion-pricing.

Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model