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Improving the Convergence of Simulation-based Dynamic Traffic Assignment Methodologies

机译：改善基于仿真的动态交通分配方法的融合

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摘要

The ability of simulation-based dynamic traffic assignment (SBDTA) models to produce reliable solutions is crucial for practical applications, particularly for those involving the comparison of modeling results across multiple scenarios. This work reviews, implements and compares novel and existing techniques for finding equilibrium solutions for SBDTA problems, focusing on their convergence pattern and stability of the results. The considered methodologies, ranging from MSA and gradient-based heuristics to column generation frameworks and partial demand loading schemes, have not been previously compared side-to-side in the literature. This research uses a single SBDTA platform to conduct such comparison on three real networks, including one with more than 200,000 trips. Most analyzed approaches were found to require a similar number of simulation runs to reach near-equilibrium solutions. However, results suggest that the quality of the results for a given convergence level may vary across methodologies.
机译：基于仿真的动态流量分配（SBDTA）模型产生可靠解决方案的能力对于实际应用至关重要，尤其是对于那些涉及跨多个场景比较建模结果的应用而言。这项工作回顾，实施和比较了寻找SBDTA问题的平衡解的新颖技术和现有技术，重点是它们的收敛模式和结果的稳定性。从MSA和基于梯度的启发式方法到列生成框架和部分需求加载方案等考虑的方法，以前没有在文献中进行过并排比较。这项研究使用单个SBDTA平台在三个真实的网络上进行这种比较，其中一个网络的旅行次数超过200,000。发现大多数分析方法都需要相似数量的模拟运行才能达到接近平衡的解决方案。但是，结果表明，在给定的收敛水平下，结果的质量可能会因方法而异。

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