Off-line calibration of dynamic traffic assignment

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Dynamic Traffic Assignment (DTA) models estimate and predict the evolution of congestion through detailed models and algorithms that capture travel demand, network supply and their complex interactions. The availability of rich time-varying traffic data spanning multiple days, collected by automatic surveillance technology, provides the opportunity to calibrate such a DTA model's many inputs and parameters so that its outputs reflect field conditions. DTA models are generally calibrated sequentially: supply model calibration (assuming known demand inputs) is followed by demand calibration with fixed supply parameters. This book develops an off-line DTA model calibration methodology for the simultaneous estimation of all demand and supply inputs and parameters, using sensor data. A complex, non-linear, stochastic optimization problem is solved, using any general traffic data. Case studies with DynaMIT, a DTA model with traffic estimation and prediction capabilities, indicate that the simultaneous approach significantly outperforms the sequential state of the art. This book is addressed to professionals and researchers who apply large-scale transportation models.