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.