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Abstract
Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression.
Highlights
Short-term traffic flow forecasting is a crucial component in many intelligent transportation systems (ITSs) [1,2,3]
This study addressed solutions for suppressing the filter divergence in the Kalman filter (KF) method of the S-Data assimilation (DA) systems resulting from these cognitive limitations. iSteslsetcattiinstgicaacl lnaosissiec icnafsoermasaatnioenx. aFmurptlhee,rtmhaotries,tthairsgleimt tirtaactkioinngcawuistehsumniisfmoramtchminogtiobnet[w37e]e,nitthweassimpouslsaitbelde vtoaliunetsuiftriovmelythdeesmtaotnesmtraotdeetlhaenpdhtehneommeeansounreomf efinlttesrads itvheerguennkcneoawrinsimngodfreolmerrcoorgsnpitriovpealgimatietaitniotnhse. cTohveaarsiasnimceilamtiaotnrixmdoudreilnsgfotrhteaKrgFetmtreatchkoidngpraoreceesxsp[r1e5s]s.eIdnaosthfoelrlowwosr:ds, as the algorithm progresses, the measurement covariance Q grows, whereas the model covariance P shrinks
Traffic flow predictions on workday Monday and non-workday Sunday for path 7078(LM862), which was part of the study area shown in Figure 5b, were computed first using the KF method, the covariance weighting (C-W)
Summary
Short-term traffic flow forecasting is a crucial component in many intelligent transportation systems (ITSs) [1,2,3]. Data assimilation (DA) is an important method that can be used to estimate the state vectors by integrating physical model information and measurements [14,15,16,17]. It can take advantage of measurements and models to make predictions by fusing measurement information during the model process based on the spatial–temporal distribution of the data and errors in the measurement and background fields [18].
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