Traffic control and management needs accurate estimation and prediction of traffic variables such as flow, speed, volume, travel time etc. Linear stochastic time series methods are powerful analytical tools. The capability of state space reconstruction makes them popular in traffic prediction and estimation. In this study, to overcome the linearity assumption of these methods, a non-linear kernel-based stochastic time series method with state space reconstruction capability is proposed. To minimise the prediction error of the method, adaptive time variant transformation from primary space to reproducing kernel Hilbert space is proposed by employing extended Kalman filter. Owing to high costs of traffic detectors, not all the metropolitan areas are equipped with these sensors; therefore in this study, an extended Kalman observer based on the new dynamic-adaptive-non-linear predictor is designed and applied for traffic flow estimation and prediction in the areas that suffer from lack of detectors. Practical data simulations and evaluations justify the high strength and accuracy of the proposed method in prediction of traffic speed with incomplete data sources.
Real time adaptive non-linear estimator/predictor design for traffic systems with inadequate detectors
IET Intelligent Transport Systems ; 8 , 3 ; 308-321
01.05.2014
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
traffic estimation , data handling , traffic prediction , Kalman filters , traffic detectors , state space reconstruction capability , real time adaptive non-linear estimator predictor design , traffic variables , real-time systems , road traffic , time series , adaptive time variant transformation , nonlinear kernel based stochastic time series method , data sources , traffic control , extended Kalman observer , metropolitan areas , traffic management , extended Kalman filter , Hilbert spaces , dynamic adaptive nonlinear predictor , traffic systems , linear stochastic time series methods , stochastic processes , inadequate detectors , state space reconstruction , nonlinear filters , kernel Hilbert space
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