On-line calibration for Dynamic Traffic Assignment models- Theory methods and application

Traffic estimation and prediction (or dynamic traffic assignment) models areexpected to contribute to the reduction of travel time delays. In this book anon-line calibration approach that jointly estimates all model parameters ispresented. The methodology imposes no restrictions on the models theparameters or the data that can be handled and emerging or future data canbe easily incorporated. The modeling approach is applicable to any simulationmodel 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 UnscentedKalman Filter. Extensive case studies on freeway networks in Europe and theUS are used to demonstrate the approach to verify the importance of on-linecalibration and to test the presented algorithms. The main target audience ofthis book comprises Intelligent Transportation Systems researchers andgraduate students as well as practitioners including Metropolitan PlanningOrganization engineers and Traffic Management Center operators and anyreader with an interest in dynamic state and parameter estimation.