Abstract
Parameter calibration of the traffic assignment models is vital to travel demand analysis and management. As an extension of the conventional traffic assignment, boundedly rational activity-travel assignment (BR-ATA) combines activity-based modeling and traffic assignment endogenously and can capture the interdependencies between high dimensional choice facets along the activity-travel patterns. The inclusion of multiple episodes of activity participation and bounded rationality behavior enlarges the choice space and poses a challenge for calibrating the BR-ATA models. In virtue of the multi-state supernetwork, this exploratory study formulates the BR-ATA calibration as an optimization problem and analyzes the influence of the two additional components on the calibration problem. Considering the temporal dimension, we also propose a dynamic formulation of the BR-ATA calibration problem. The simultaneous perturbation stochastic approximation algorithm is adopted to solve the proposed calibration problems. Numerical examples are presented to calibrate the activity-based travel demand for illustrations. The results demonstrate the feasibility of the solution method and show that the parameter characterizing the bounded rationality behavior has a significant effect on the convergence of the calibration solutions.
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