Simulation-based dynamic traffic assignment (DTA) models play a vital role in transportation planning and operations. While the widely studied equilibrium-seeking DTA including dynamic user equilibrium (DUE) often provides robust and consistent outcomes, their expensive computational cost for large-scale network applications has been a burden in practice. The noniterative stochastic route choice (SRC) model, as a nonequilibrium seeking DTA model, provides an alternative for specific transportation operations applications that may not require equilibrium results after all (e.g., evacuation and major network disruptions) and thus tend to be computationally less expensive, yet may suffer from inconsistent outcomes. While DUE is a widely accepted approach for many strategic planning applications, SRC has been increasingly used in practice for traffic operations purposes. This paper aims to provide a comparative and quantitative analysis of the two modeling approaches. Specifically, a comparison has been made at two levels: link-level flows and network-level congestion patterns. Results suggest that adaptive driving improves the quality of the SRC solution, but its difference from DUE still remains significant at the link level. Results have practical implications for the application of large-scale simulation-based DTA models for planning and operations purposes.

中文翻译：

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在基于仿真的动态交通分配模型中比较动态用户平衡和非迭代随机路线选择：大型网络的实际考虑

基于仿真的动态交通分配（DTA）模型在交通规划和运营中起着至关重要的作用。尽管经过广泛研究的包括动态用户均衡（DUE）在内的寻求均​​衡的DTA通常可以提供可靠且一致的结果，但它们在大型网络应用中的昂贵计算成本实际上已成为一种负担。非迭代随机路线选择（SRC）模型作为非平衡寻求DTA模型，为特定的运输运营应用提供了替代方案，这些应用毕竟可能不需要平衡结果（例如，疏散和主要网络中断），因此在计算上往往更便宜，但结果可能会不一致。尽管DUE是许多战略规划应用程序中广泛接受的方法，SRC在实践中已越来越多地用于交通运营目的。本文旨在提供对这两种建模方法的比较和定量分析。具体而言，已在两个级别进行了比较：链路级别的流量和网络级别的拥塞模式。结果表明，自适应驱动可以提高SRC解决方案的质量，但是它与DUE的区别在链接级别仍然很明显。结果对于将大型基于模拟的DTA模型用于计划和运营目的具有实际意义。结果表明，自适应驱动可以提高SRC解决方案的质量，但是它与DUE的区别在链接级别仍然很明显。结果对于将大型基于模拟的DTA模型应用于计划和运营目的具有实际意义。结果表明，自适应驱动可以提高SRC解决方案的质量，但是它与DUE的区别在链接级别仍然很明显。结果对于将大型基于模拟的DTA模型用于计划和运营目的具有实际意义。