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
models 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...