PC–SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration
PC–SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration
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DOI：
10.1109/tits.2019.2915273
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发表时间：
2020-04
影响因子：
8.5
通讯作者：
Moeid Qurashi;T. Ma;Emmanouil Chaniotakis;C. Antoniou
中科院分区：
文献类型：
--
作者：
Moeid Qurashi;T. Ma;Emmanouil Chaniotakis;C. Antoniou
校准和验证一直是交通模型开发中的重要课题。事实上，当移动到动态交通分配（DTA）模型，需要动态更新的需求和供应组件创建一个相当大的负担，现有的校准算法，往往使他们不切实际。这些校准方法大多受到限制，无论是由于非线性或增加的问题维数。同时扰动随机近似（SPSA）已经提出了DTA模型的校准，令人鼓舞的结果，超过十年。然而，随着问题规模和复杂性的增加，它往往不能合理地收敛。本文将联合收割机SPSA与主成分分析（PCA）相结合，提出了一种新的SPSA算法--PC-SPSA。PCA将SPSA的搜索区域限制在从较低维度的历史估计中捕获的结构关系内，从而降低了问题的大小和复杂性。我们制定的算法，演示其操作，并探讨其性能使用城市网络的维托里亚，西班牙。从不同变量的规模和界定其值出现的实际问题也通过使用非线性合成函数的敏感性分析进行了分析。
Calibration and validation have long been a significant topic in traffic model development. In fact, when moving to dynamic traffic assignment (DTA) models, the need to dynamically update the demand and supply components creates a considerable burden on the existing calibration algorithms, often rendering them impractical. These calibration approaches are mostly restricted either due to non-linearity or increasing problem dimensionality. Simultaneous perturbation stochastic approximation (SPSA) has been proposed for the DTA model calibration, with encouraging results, for more than a decade. However, it often fails to converge reasonably with the increase in problem size and complexity. In this paper, we combine SPSA with principal components analysis (PCA) to form a new algorithm, we call, PC–SPSA. The PCA limits the search area of SPSA within the structural relationships captured from historical estimates in lower dimensions, reducing the problem size and complexity. We formulate the algorithm, demonstrate its operation, and explore its performance using an urban network of Vitoria, Spain. The practical issues that emerge from the scale of different variables and bounding their values are also analyzed through a sensitivity analysis using a non-linear synthetic function.