Traffic management applications are supported by dynamic models fed with realistic real-time demand matrices in order to find efficient solutions in the short-term horizon. OD matrices have to be estimated from historic or real-time data collection and prior matrices. In this work, we present a simulation-optimization Bilevel-DUE approach for Dynamic OD matrix estimation suitable for off-line application, in which the lower level consists on a dynamic user equilibrium problem solved by a mesoscopic simulator. Improvements as the consideration of travel times and the use of a conjugate gradient combined with trust region scheme are introduced and analyzed. Time-sliced OD matrices generated off-line with the former procedure from historic data are suitable initializations for on-line and real-time dynamic OD estimation methods based on Kalman filtering. Some of those methods were developed by the authors in previous works and they provide realistic matrices to cope with day-to-day demand variability and real-time traffic conditions.

Peer Reviewed