Forecasting Under Data Sparsity

Forecasting Under Data Sparsity is a predictive modeling framework in the domain of traffic participant behavior prediction that specifically addresses the challenge of generating accurate future trajectory estimates when limited observational data is available for training or inference.

36

Publications

1.4K

Citations

88

Authors

39

Institutions

Publications per year

2017–2026

28

Authors

88

  1. LO
    Luca Onorante

    European Central Bank, Germany

    2 publications · 101 citations

    2

    h-index

  2. GK
    Gary Koop

    University of Strathclyde, United Kingdom

    2 publications · 101 citations

    2

    h-index

  3. PJ
    Patrick Jaillet

    Massachusetts Institute of Technology, United States

    2 publications · 65 citations

    2

    h-index

  4. FH
    Florian Huber

    University of Salzburg, Austria

    2 publications · 101 citations

    2

    h-index

  5. JL
    Jun Luo

    Lenovo (China), China

    1 publications · 18 citations

    1

    h-index

1–5 of 88

Venues

  1. 4

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  2. IEEE Access

    2 publications · 78 citations

    2

    h-index

  3. 1

    h-index

  4. MDPI (MDPI AG)

    Repository · 1 publications · 31 citations

    1

    h-index

  5. Astin Bulletin

    1 publications · 13 citations

    1

    h-index

1–5