An approach for filter divergence suppression in a sequential data assimilation system and its application in short-term traffic flow forecasting

Xiaohua Tong, Runjie Wang, Wenzhong Shi, Zhiyuan Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Article numberijgi9060340
JournalISPRS International Journal of Geo-Information
Issue number6
Publication statusPublished - Jun 2020


  • Filter divergence
  • Gain matrix
  • L1-norm constrained
  • Sequential data assimilation system
  • Short-term traffic flow forecasting

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

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