Accelerated on-line calibration of Dynamic Traffic Assignment using distributed Stochastic Gradient approximation

Dynamic Traffic Assignment (DTA) system [Ben-Akiva et al., 1991] [Mahmassani, 2001] benefits travelers by providing accurate estimate of current traffic conditions, consistent anticipatory network information as well as reliable route guidance. Over the years, two types of model adjustment schemes have been studied - DTA off-line calibration [Balakrishna, 2006] [Toledo et al., 2003] [van der Zijpp, 1997] and DTA on-line calibration [Antoniou et al., 2007] [Wang et al., 2007] [Ashok and Ben-Akiva, 2000]. The on-line calibration of DTA system allows real-time model self-corrections and has proven to be a useful complement to off-line calibration. In this paper, we explore distributed gradient calculations for the speed-up of on-line calibration of Dynamic Traffic Assignment (DTA) systems. Extended Kalman Filter (EKF) and Stochastic Gradient Descent (GD) are examined and their corresponding distributed versions (Para-EKF and Para-GD) are proposed. A case study is performed on a 25-km expressway in Western Portugal. We empirically show that the application of distributed gradient calculation significantly reduce the computational time of on-line calibration and thus provide attractive alternatives for speed-critical real-time DTA systems.