Efficiently solving the user equilibrium traffic assignment problem with elastic demand(UE-TAPED)for transportation networks is a critical problem for transportation studies.Most existing UE-TAPED algorithms are desig...Efficiently solving the user equilibrium traffic assignment problem with elastic demand(UE-TAPED)for transportation networks is a critical problem for transportation studies.Most existing UE-TAPED algorithms are designed using a sequential computing scheme,which cannot take advantage of advanced parallel computing power.Therefore,this study focuses on model decomposition and parallelization,proposing an origin-based formulation for UE-TAPED and proving an equivalent reformulation of the original problem.Furthermore,the alternative direction method of multipliers(ADMM)is employed to decompose the original problem into independent link-based subproblems,which can solve large-scale problems with small storage space.In addition,to enhance the efficiency of our algorithm,the parallel computing technology with optimal parallel computing schedule is implemented to solve the link-based subproblems.Numerical experiments are performed to validate the computation efficiency of the proposed parallel algorithm.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52302391,5202375,and 52131203)the Natural Science Foundation of Jiangsu Province,China(No.BK20210247)the Fundamental Research Funds for the Central Universities,China(No.2242022R40025).
文摘Efficiently solving the user equilibrium traffic assignment problem with elastic demand(UE-TAPED)for transportation networks is a critical problem for transportation studies.Most existing UE-TAPED algorithms are designed using a sequential computing scheme,which cannot take advantage of advanced parallel computing power.Therefore,this study focuses on model decomposition and parallelization,proposing an origin-based formulation for UE-TAPED and proving an equivalent reformulation of the original problem.Furthermore,the alternative direction method of multipliers(ADMM)is employed to decompose the original problem into independent link-based subproblems,which can solve large-scale problems with small storage space.In addition,to enhance the efficiency of our algorithm,the parallel computing technology with optimal parallel computing schedule is implemented to solve the link-based subproblems.Numerical experiments are performed to validate the computation efficiency of the proposed parallel algorithm.
