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

Moeid Qurashi · Tao Ma · Emmanouil Manos Chaniotakis · Constantinos Antoniou

37 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Transportation Planning and Optimization Traffic control and management Traffic Prediction and Management Techniques

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.

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References

1998Implementation of the simultaneous perturbation algorithm for stochastic optimization 1998An Overview of the Simultaneous Perturbation Method for Efficient Optimization 2000Adaptive stochastic approximation by the simultaneous perturbation method 1993Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts 2014An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models 2015Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation 2010A gradient approximation approach for adjusting temporal origin–destination matrices 2006Off-line calibration of Dynamic Traffic Assignment models

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