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PC-SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration

Qurashi, M; Ma, T; Chaniotakis, E; Antoniou, C; (2019) PC-SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration. IEEE Transactions on Intelligent Transportation Systems 10.1109/tits.2019.2915273. (In press). Green open access

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Abstract

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.

Type: Article
Title: PC-SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tits.2019.2915273
Publisher version: https://doi.org/10.1109/tits.2019.2915273
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Model calibration, principal component analysis (PCA), simultaneous perturbation stochastic approximation (SPSA)
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10093641
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