Real-time Traffic Forecasting

Real-Time Traffic Forecasting is a traffic demand forecasting method that distinguishes itself by predicting traffic conditions instantaneously or with minimal latency, using continuously updated, near real-time sensor and communication data to support immediate decision-making in intelligent transportation systems.

620

Publications

34.3K

Citations

1.9K

Authors

613

Institutions

Publications per year

2017–2026

322

Authors

1.9K

  1. LV
    Lelitha Vanajakshi

    Indian Institute of Technology Madras, India

    6 publications · 322 citations

    6

    h-index

  2. TZ
    Teng Zhou

    Shantou University, China

    5 publications · 446 citations

    5

    h-index

  3. CS
    Cyrus Shahabi

    University of Southern California, United States

    5 publications · 358 citations

    5

    h-index

  4. MA
    Mohamed Abdel‐Aty

    University of Central Florida, United States

    4 publications · 292 citations

    5

    h-index

  5. MB
    Moshe Ben‐Akiva

    Massachusetts Institute of Technology, United States

    4 publications · 177 citations

    4

    h-index

1–5 of 1.9K

Venues

  1. 22

    h-index

  2. 18

    h-index

  3. 16

    h-index

  4. IEEE Access

    10 publications · 380 citations

    11

    h-index

  5. Knowledge-Based Systems

    9 publications · 449 citations

    10

    h-index

1–5