This paper presents two novel algorithmic frameworks to address the logit-based stochastic user equilibrium traffic assignment problem (SUE-TAP). Following the different variant of the gradient projection (termed as GP2) algorithm, we propose an improved GP2 algorithm (IGP) for the SUE-TAP. This study initially presents a smart approach for determining the allocation of more or less effort to specific origin–destination (OD) pairs. Subsequently, the TAP can be decomposed by different OD pairs, whereas the proposed IGP algorithm is designed based on the serial scheme (i.e., the Gauss–Seidel method). Therefore, a new parallel algorithm P-IGP is proposed, which integrates the block coordinate descent (BCD) method and the IGP algorithm. In specific, the independent OD pairs can be separated into several blocks, and the OD-based restricted subproblems within each block can be solved in parallel. Then, we outline the entire process of implementing the P-IGP algorithm to address the SUE-TAP. Several numerical experiments are conducted to verify the proposed algorithms. The results reveal that the proposed IGP algorithm demonstrates significantly speeder convergence in comparison to the traditional GP2 algorithm, achieving a remarkable acceleration of approximately 12%. Furthermore, the performance of the P-IGP algorithm surpasses that of the proposed IGP algorithm, and it can further achieve a notable 4–5-fold enhancement in convergence efficiency.

本文提出了两种新颖的算法框架来解决基于逻辑的随机用户均衡流量分配问题（SUE-TAP）。遵循梯度投影（称为 GP2）算法的不同变体，我们为 SUE-TAP 提出了一种改进的 GP2 算法（IGP）。这项研究最初提出了一种智能方法，用于确定将更多或更少的努力分配给特定的起点-目的地（OD）对。随后，TAP可以由不同的OD对分解，而所提出的IGP算法是基于串行方案（即Gauss-Seidel方法）设计的。因此，提出了一种新的并行算法P-IGP，该算法集成了块坐标下降(BCD)方法和IGP算法。具体来说，可以将独立的OD对分成多个块，并且可以并行求解每个块内的基于OD的受限子问题。然后，我们概述了实现P-IGP算法来解决SUE-TAP的整个过程。进行了多次数值实验来验证所提出的算法。结果表明，与传统的 GP2 算法相比，所提出的 IGP 算法的收敛速度显着加快，实现了约 12% 的显着加速。此外，P-IGP算法的性能超越了所提出的IGP算法，并且收敛效率可以进一步实现4-5倍的显着提升。