A General Primal-Dual Modeling Framework for Stochastic Traffic Assignment Problems
作者： Chi Xie
DOI:
关键词: Range (mathematics) 、 Set (abstract data type) 、 Feature (machine learning) 、 Mathematics 、 Mathematical optimization 、 Stochastic process 、 Weapon target assignment problem 、 DUAL (cognitive architecture) 、 Primal dual 、 Unconstrained optimization
摘要: This paper discusses a set of traffic assignment problems with route choices specified by random utility theory in primal-dual modeling framework. The framework presents such common functional form that can accommodate wide range different problems. Particular attention is given to the dual formulation its unconstrained feature opens door applying optimization algorithms for embraced Numerical examples are provided support insights and facts derived from primal formulations model stochastic system-optimal user-equilibrium justify conjugate relationship between models.
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