Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/87935
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Tong, X | - |
dc.creator | Wang, R | - |
dc.creator | Shi, W | - |
dc.creator | Li, Z | - |
dc.date.accessioned | 2020-09-04T00:52:56Z | - |
dc.date.available | 2020-09-04T00:52:56Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/87935 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Tong X, Wang R, Shi W, Li Z. An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting. ISPRS International Journal of Geo-Information. 2020; 9(6):340, is available at https://doi.org/10.3390/ijgi9060340 | en_US |
dc.subject | Filter divergence | en_US |
dc.subject | Gain matrix | en_US |
dc.subject | L1-norm constrained | en_US |
dc.subject | Sequential data assimilation system | en_US |
dc.subject | Short-term traffic flow forecasting | en_US |
dc.title | An approach for filter divergence suppression in a sequential data assimilation system and its application in short-term traffic flow forecasting | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 9 | - |
dc.identifier.issue | 6 | - |
dc.identifier.doi | 10.3390/ijgi9060340 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | ISPRS international journal of geo-information, 2020, v. 9, no. 6, ijgi9060340 | - |
dcterms.isPartOf | ISPRS international journal of geo-information | - |
dcterms.issued | 2020 | - |
dc.identifier.scopus | 2-s2.0-85085664093 | - |
dc.identifier.eissn | 2220-9964 | - |
dc.identifier.artn | ijgi9060340 | - |
dc.description.validate | 202009 bcma | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Tong_approach_filter_divergence.pdf | 1.19 MB | Adobe PDF | View/Open |
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