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From theory to practice II: a comprehensive approach for the sensitivity analysis of high dimensional and computationally expensive traffic simulation models

机译：从理论到实践II：一种综合方法，用于高维和计算昂贵的交通仿真模型的敏感性分析

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The reliability of traffic model results is strictly connected to the quality of its calibration. A challenge arising in this context concerns the selection of the most influential input parameters. A model Sensitivity Analysis (SA) should be used with this aim. Unfortunately, due to the limitation of time and computational resources, a proper SA is hardly performed in the common practice. A recent study introduced a methodology based on Gaussian process meta-models for the SA of computationally expensive traffic simulation models. Its main limitation was, however, its dependence on the model dimensionality. When the model has more than 15~20 parameters (depending on its regularity), the estimation of a Gaussian process meta-model (also known as Kriging meta-model) may become problematic. In this light, the SA of high-dimensional and computationally expensive models still remains an issue. In the present paper, the Kriging-based approach has been coupled with another recently developed approach (the quasi-OTEE) for the SA of computationally expensive models. The quasi-OTEE SA can be used to identify the whole sub-set of sensitive parameters of a high-dimensional model, and the Kriging-based SA can then be used to refine the analysis and rank the different parameters of the sub-set in a more reliable way. The application of this new SA method is illustrated with the Wiedemann-74 car-following model. Results show that the new method requires 40 times less model evaluations than a standard variance-based SA in identify the influential parameters and their ranks.
机译：交通模型结果的可靠性严格连接到其校准的质量。在此背景下产生的挑战涉及选择最有影响力的输入参数。应与此目的一起使用模型敏感性分析（SA）。遗憾的是，由于时间和计算资源的限制，在常识中几乎没有执行合适的SA。最近的一项研究介绍了一种基于高斯过程元模型的方法，用于计算昂贵的交通仿真模型的SA。然而，其主要限制是对模型维度的依赖性。当模型具有超过15〜20个参数（取决于其规则性）时，高斯过程元模型的估计（也称为Kriging Meta-Model）可能会出现问题。在这种光明中，高维和计算昂贵模型的SA仍然是一个问题。在本文中，基于Kriging的方法已经与另一个最近开发的方法（准otee）耦合，用于计算昂贵的模型的SA。准oteeSA可用于识别高维模型的整个敏感参数的整个子集，然后可以使用基于Kriging的SA来改进分析并排列子集的不同参数一种更可靠的方式。该新SA方法的应用用Wiedemann-74车辆跟踪模型说明。结果表明，新方法比标准方差的SA识别有影响力的参数及其排名需要40倍的模型评估。

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