Solving a dynamic traffic assignment problem in a transportation network is a computational challenge. This study first reviews the different algorithms in the literature used to numerically calculate the user equilibrium (UE) related to dynamic network loading. Most of them are based on iterative methods to solve a fixed‐point problem. Two elements must be computed: the path set and the optimal path flow distribution between all origin–destination pairs. In a generic framework, these two steps are referred to as the outer and the inner loops, respectively. The goal of this study is to assess the computational performance of the inner loop methods that calculate the path flow distribution for different network settings (mainly network size and demand levels). Several improvements are also proposed to speed up convergence: four new swapping algorithms and two new methods for the step size initialization used in each descent iteration. All these extensions significantly reduce the number of iterations to obtain a good convergence rate and drastically speed up the overall simulations. The results show that the performance of different components of the solution algorithm is sensitive to the network size and saturation. Finally, the best algorithms and settings are identified for all network sizes with particular attention being given to the largest scale.

中文翻译：

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收敛算法的交叉比较解决基于行程的动态交通分配问题

解决交通网络中的动态交通分配问题是计算上的挑战。这项研究首先回顾了文献中用于数值计算与动态网络负载相关的用户平衡（UE）的不同算法。它们大多数基于迭代方法来解决定点问题。必须计算两个元素：路径集和所有原点－目的地对之间的最佳路径流分布。在通用框架中，这两个步骤分别称为外部循环和内部循环。这项研究的目的是评估内环方法的计算性能，该方法计算不同网络设置（主要是网络规模和需求水平）的路径流量分布。为了加快收敛速度​​，还提出了一些改进措施：每次下降迭代中使用的四种新的交换算法和两种新的步长初始化方法。所有这些扩展都显着减少了迭代次数，从而获得了良好的收敛速度，并大大加快了整体仿真的速度。结果表明，求解算法不同组件的性能对网络大小和饱和度敏感。最后，针对所有网络规模确定最佳算法和设置，并特别注意最大规模。结果表明，求解算法不同组件的性能对网络大小和饱和度敏感。最后，针对所有网络规模确定最佳算法和设置，并特别注意最大规模。结果表明，求解算法不同组件的性能对网络大小和饱和度敏感。最后，针对所有网络规模确定最佳算法和设置，并特别注意最大规模。