Deep Learning-based Forecasting

Deep Learning-Based Forecasting is a traffic demand forecasting method that leverages deep neural network architectures to automatically learn complex spatiotemporal patterns from large-scale transportation data, distinguishing it from traditional statistical or shallow machine learning approaches.

3.6K

Publications

267.6K

Citations

12K

Authors

2.8K

Institutions

Publications per year

2017–2026

3.5K

Authors

12K

  1. JS
    Jaydip Sen

    Tata Consultancy Services (India), India

    14 publications · 590 citations

    12

    h-index

  2. SM
    Sidra Mehtab

    Association for Computing Machinery, United States

    13 publications · 535 citations

    11

    h-index

  3. YW
    Yinhai Wang

    University of Washington, United States

    12 publications · 2.2K citations

    11

    h-index

  4. WJ
    Weiwei Jiang

    Beijing University of Posts and Telecommunications, China

    10 publications · 1.3K citations

    10

    h-index

  5. RC
    Ravinesh C. Deo

    University of Southern Queensland, Australia

    10 publications · 517 citations

    10

    h-index

1–5 of 12K

Venues

  1. IEEE Access

    189 publications · 15.1K citations

    69

    h-index

  2. 45

    h-index

  3. arXiv (Cornell University)

    Repository · 135 publications · 8.5K citations

    44

    h-index

  4. Energies

    75 publications · 5.4K citations

    38

    h-index

  5. Expert Systems with Applications

    60 publications · 6.8K citations

    37

    h-index

1–5