Data - Model Synchronization in Extended Kalman Filters for Accurate Online Traffic State Estimation
نویسندگان
چکیده
Real-time freeway traffic state estimation plays an important role in Dynamic Traffic Management (DTM) and Advanced Traveler Information Systems (ATIS). One of the model-driven estimation techniques used in practice is based on the Extended Kalman Filter (EKF). It consists of two components: the Predictor forecasts the traffic state over a short period of time, usually a few seconds; the Corrector fuses the traffic state with online data, observed by induction loops, which are usually aggregated over one minute. Currently, in many approaches, the traffic state is corrected only once, namely as soon as the observation becomes available. In this paper it is shown that this correction time scheme does not fully make use of all the available data. A correction time scheme is proposed, which synchronizes the Correction step of the EKF with the aggregation period of the data. Experiments with both synthetic and real data show improvements in the estimation quality. Schreiter, Van Hinsbergen, Zuurbier, Van Lint, Hoogendoorn 1
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