Paper Details

TRB 91st Annual Meeting (January 22-26, 2012)

Event Number:	711
Event Title:	Innovations in Activity and Travel Behavior
Event Date:	Jan 25 2012 8:30AM- 10:15AM
Event Location:	Hilton, International Center
Event Description:
Event Agenda:

Positive Model of Departure Time and Peak Spreading Dynamics (12-4514)

This paper develops apositive (in contrast to normative) approach for modeling departure timedynamics at the individual levels, and analyzes the consequent system-level peak spreading effects. The positive modeling approach avoids assumptions of substantial rationality, and focuses on how individuals actually make departure time choices. The proposed analyticalframework theorizeshowheterogeneous users learn time-dependent travel conditions, accumulate relevant spatial-temporal knowledge, form subjective beliefs,search for alternative departure times under sufficiently large stimuli, and adjust departure timesbased on subjective beliefs and decision rules. Following the theoretical framework,we specifylearning with Bayesian methods, empirically estimates search start and stopping conditions that vary among users, and empirically derivesearch and decision rules from a joint reveal/stated-preference survey dataset. The resulting quantitative model is demonstrated with a numerical example. To enable the application of the proposed positive modeling approach, a low-cost and practical memory-recall survey method has also been developed to provide necessary behavioral process data for model estimation and validation. In addition, the individual-level departure time choice model is ready for real-world applications, and can be integrated with microscopic traffic simulation, simulation-based dynamic traffic assignment, and/or activity/agent-based demand models.

Authors
     Zhang, Lei , University of Maryland, College Park
     Xiong, Chenfeng , University of Maryland, College Park