An ADMM-based parallel algorithm for solving traffic assignment problem with elastic demand

Abstract

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.