The aim of this study is to solve the large‐scale dynamic traffic assignment (DTA) model using a simulation‐based framework, which is computationally a challenging problem. Many studies have been performed on developing an efficient algorithm to solve DTA. Most of the existing algorithms are based on path‐swapping descent direction methods. From the computational standpoint, the main drawback of these methods is that they cannot be parallelized. This is because the existing algorithms need to know the results of the last iteration to determine the next best path flow for the next iteration. Thus, their performance depends on the single initial or intermediate solution, which means they exploit a solution that satisfies the equilibrium conditions more than explore the solution space for the optimal solution. More specifically, the goal of this study is to overcome the drawbacks of serial algorithms by using meta‐heuristic algorithms known to be parallelizable and that have never been applied to the simulation‐based DTA problem. This study proposes two new solution methods: a new extension of the simulated annealing and an adapted genetic algorithm. With parallel simulation, the algorithm runs more simulations in comparison with existing methods, but the algorithm explores the solution space better and therefore obtains better solutions in terms of closeness to the optimal solution and computation time compared to classical methods.

中文翻译：

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基于仿真的动态流量分配：并行计算的元启发式解决方法

这项研究的目的是使用基于仿真的框架来解决大规模动态交通分配（DTA）模型，这在计算上是一个具有挑战性的问题。在开发有效的算法以解决DTA方面已经进行了许多研究。现有的大多数算法都基于路径交换下降方向方法。从计算的角度来看，这些方法的主要缺点是它们无法并行化。这是因为现有算法需要知道上一次迭代的结果，才能确定下一次迭代的下一个最佳路径流。因此，它们的性能取决于单个初始或中间解决方案，这意味着它们要利用满足平衡条件的解决方案，而不是为最佳解决方案探索解决方案空间。进一步来说，本研究的目的是通过使用已知可并行化且从未应用于基于仿真的DTA问题的元启发式算法来克服串行算法的弊端。这项研究提出了两种新的解决方法：模拟退火的新扩展和自适应遗传算法。与并行方法相比，与现有方法相比，该算法运行更多的模拟，但是与传统方法相比，该算法更好地探索了求解空间，因此在与最佳解的接近度和计算时间方面获得了更好的解。模拟退火的新扩展和自适应遗传算法。与并行方法相比，与现有方法相比，该算法运行更多的模拟，但是与传统方法相比，该算法更好地探索了求解空间，因此在与最佳解的接近度和计算时间方面获得了更好的解。模拟退火的新扩展和自适应遗传算法。与并行方法相比，与现有方法相比，该算法运行更多的模拟，但是与传统方法相比，该算法更好地探索了求解空间，因此在与最佳解的接近度和计算时间方面获得了更好的解。