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

Abstract

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.

KEYWORDS:

• Activity-travel
• machine-learning
• daily activity
• travel behavior
• travel demand

Acknowledgements

We would like to thank the Dalhousie Transportation and Environmental Simulation Studies (TESS) group members for their valuable suggestions. Data for this research were provided by the Halifax STAR Project, supported through the Atlantic Innovation Fund from the Atlantic Canada Opportunities Agency, Project No.181930. We would also like to thank anonymous reviewers for their very useful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author Contributions

The authors confirm contribution to the article as follows: study conceptualization, experimental design, statistical analysis, data visualization, and results’ interpretation: Mohammad Hesam Hafezi, Naznin Sultana Daisy; draft article preparation: Mohammad Hesam Hafezi, Naznin Sultana Daisy; revision: Mohammad Hesam Hafezi, Naznin Sultana Daisy, Hugh Millward. All authors reviewed the results and approved the final version of the manuscript.