MULTI-DAY NEED-BASED MODEL

Team: Sally (Kexin) Chen, Ravi Seshadri, Varun Pattabhiraman, Maya Abou Zeid, Ali Shamshiripour, Yusuke Hara, Youssef Medhat Aboutaleb, Takanori  Sakai, Carlos Carrion, Moshe Ben-Akiva

• Develop a behavioral model based on the need theory

• Propose a model that jointly describes the choices of activity location, duration, and frequency in multiple days.

• Formulate an optimization problem that maximizes the average psychological inventory which describe the need level of individuals.

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Based on the theory of needs, we develop a behavioral model with the objective to maximize need satisfaction, which describes, in a joint manner, the choices of activity location, duration, and frequency in multiple days (e.g. a week). The need is associated with a psychological inventory reflecting the level of satisfaction of the need. The activity that satisfies “need” replenishes the psychological inventory by a quantity defined by the Activity Production Function. The inventory is consumed overtime which indicates that the need level increases. We formulate an optimization problem that maximizes the average psychological inventory subject to a time constraint.

The proposed methodology will allow us to understand and replicate the interactions between activities of different days in a defined period. This is increasingly important as the activities of individuals have become more flexible (e.g. work-from-home, flexible work schedule, and online shopping) while smart transportation solutions (such as dynamic congestion pricing) need to be designed based on the understanding of complex interactions of activity decisions people make. The model will contribute to the development of the state-of-the-art transportation simulators.

Publications:

Pattabhiraman, V. R. (2012). A needs-based approach to activity generation for travel demand analysis (Doctoral dissertation, Massachusetts Institute of Technology).