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An inertia-infused ADMM-based splitting algorithm with parallel computing for traffic assignment
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DOI：10.1080/19427867.2025.2564421.png)
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Abstract
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In this paper, we propose an inertia-infused alternating direction method of multipliers (ADMM)-based splitting algorithm for the origin-based traffic assignment problem. The method is framed as a sequential Gauss-Seidel update with Jacobi-type parallelization in each subproblem. A Nesterov-accelerated inertial strategy, using information from previous iterations, is applied before updating link flows. Within each decomposed block, link-flow subproblems are solved in parallel via the gradient projection method with inertia. In updating Lagrange multipliers, a nonnegative relaxation factor is incorporated to improve flexibility. Numerical experiments show that with properly chosen inertial and relaxation parameters, the proposed algorithm achieves superior performance compared with the original ADMM.
Keywords:
Traffic assignment
user equilibrium
alternating direction method of multipliers
inertial strategy
parallel computing mode
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