Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting.

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  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel, Switzerland : MDPI, c2000-
    • Subject Terms:
      Forecasting*
    • Abstract:
      Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications.
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    • Grant Information:
      51878166 National Natural Science Foundation of China
    • Contributed Indexing:
      Keywords: decomposition-reconstruction; intelligent transportation system; interpretability; predictability; traffic state forecasting
    • Publication Date:
      Date Created: 20220727 Date Completed: 20220728 Latest Revision: 20220731
    • Publication Date:
      20240105
    • Accession Number:
      PMC9319391
    • Accession Number:
      10.3390/s22145263
    • Accession Number:
      35890944