Formulation and solution for calibrating boundedly rational activity-travel assignment:An exploratory study

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 BRATA 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.