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On-line calibration for Dynamic Traffic Assignment models- Theory, methods and application (Paperback)
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On-line calibration for Dynamic Traffic Assignment models- Theory, methods and application (Paperback)
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Traffic estimation and prediction (or dynamic traffic assignment)
models are expected to contribute to the reduction of travel time
delays. In this book, an on-line calibration approach that jointly
estimates all model parameters is presented. The methodology
imposes no restrictions on the models, the parameters or the data
that can be handled, and emerging or future data can be easily
incorporated. The modeling approach is applicable to any simulation
model and is not restricted to the application domain covered in
this book. Several modified, non-linear Kalman Filter methodologies
are presented, e.g. Extended Kalman Filter (EKF), Iterated EKF,
Limiting EKF, and Unscented Kalman Filter. Extensive case studies
on freeway networks in Europe and the US are used to demonstrate
the approach, to verify the importance of on-line calibration, and
to test the presented algorithms. The main target audience of this
book comprises Intelligent Transportation Systems researchers and
graduate students, as well as practitioners, including Metropolitan
Planning Organization engineers and Traffic Management Center
operators, and any reader with an interest in dynamic state and
parameter estimation.
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