An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models

• 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.

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.