Techniques for improving the effectiveness of the SPSA algorithm in dynamic demand calibration

The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors. To overcome this drawback, some authors suggested modifications of basic SPSA to improve its performance. In this paper, the authors investigate various techniques and approaches to improve the SPSA performance, and overcome, or at least alleviate, its shortcomings. The authors concentrate their analysis mostly on SPSA coefficients and gradient control. The comparison of investigated settings is conducted on a real-world network. This establishes a path to identify critical aspects that influence the calibration process and suggests an optimal SPSA configuration for practice. The contribution of this paper is to provide a detailed analysis of the SPSA behavior in cases its configuration is subject to various modifications. The findings are primarily intended for the off-line context. However, the insights can also be used for the selection of the most efficient SPSA configuration given time constraint, particularly suitable for on-line applications.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 368-373
  • Monograph Title: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2017)

Subject/Index Terms

Filing Info

  • Accession Number: 01757591
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 13 2020 9:28AM