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Speed Estimation from Single Loop Data Using an Unscented Particle Filter
Author(s) -
Ye Zhirui,
Zhang Yunlong
Publication year - 2010
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2009.00634.x
Subject(s) - unscented transform , ensemble kalman filter , kalman filter , extended kalman filter , control theory (sociology) , alpha beta filter , invariant extended kalman filter , computer science , fast kalman filter , auxiliary particle filter , algorithm , moving horizon estimation , artificial intelligence , control (management)
This article presents a hybrid method, the Unscented Particle Filter (UPF), for traffic flow speed estimation using single loop outputs. The Kalman filters used in past speed estimation studies employ a Gaussian assumption that is hardly satisfied. The hybrid method that combines a parametric filter (Unscented Kalman Filter) and a nonparametric filter (Particle Filter) is thus proposed to overcome the limitations of the existing methods. To illustrate the advantage of the proposed approach, two data sets collected from field detectors along with a simulated data set are utilized for performance evaluation and comparison with the Extended Kalman Filter and the Unscented Kalman Filter. It is found that the proposed method outperforms the evaluated Kalman filter methods. The UPF method produces accurate speed estimation even for congested flow conditions in which many other methods have significant accuracy problems .