An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models. (February 2015) - Lu, Lu; Xu, Yan; Antoniou, Constantinos; Ben-Akiva, Moshe
Highlights: W-SPSA, an enhanced SPSA algorithm that limits the impact of noise, is presented. W-SPSA incorporates information of spatial and temporal correlation in a traffic network to a «weight» matrix. Implementation details in the context of Dynamic Traffic Assignment (DTA) models are discussed. Benefits of W-SPSA are illustrated in a case study of the entire expressway network in Singapore. W-SPSA appears to outperform the original SPSA algorithm. Abstract: Simultaneous perturbation stochastic approximation (SPSA) is an efficient and well established optimization method that approximates gradients from successive objective function evaluations. It is especially attractive for high-dimensional problems and has been successfully applied to the calibration of Dynamic Traffic Assignment (DTA) models. This paper presents an enhanced SPSA algorithm, called Weighted SPSA (W-SPSA), which incorporates the information of spatial and temporal correlation in a traffic network to limit the impact of noise and improve convergence and robustness. W-SPSA appears to outperform the original SPSA algorithm by reducing the noise generated by uncorrelated measurements in the gradient approximation, especially for DTA models of sparsely correlated large-scale networks and a large number of time intervals. Comparisons between SPSA and W-SPSA have been performed through rigorous synthetic tests and the application of W-SPSA for the calibration of real world DTA networks is demonstrated with a case study of the entire expressway network in Singapore.