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
Parents
620
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Ling Zhao, Yujiao Song, Chao Zhang et al. · IEEE Transactions on Intelligent Transportation Systems · 2019 · 3.2K citations · Full text
Computing, Data Science, Forecasting +16
Driving with knowledge from the physical world
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1.9K
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613
Tsinghua University
Beijing, China
12 h-index
30 publications
2.5K citations
Beijing Jiaotong University
Beijing, China
12 h-index
29 publications
1.5K citations

Southeast University
Nanjing, China
12 h-index
34 publications
1.6K citations
National Technical University of Athens
Athens, Greece
11 h-index
19 publications
2.4K citations
Wuhan University
Wuhan, China
8 h-index
23 publications
814 citations
University of Maryland, College Park
College Park, United States
8 h-index
24 publications
2K citations

Beihang University
Beijing, China
8 h-index
18 publications
1.8K citations

Beijing University of Posts and Telecommunications
Beijing, China
8 h-index
22 publications
1.1K citations
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23 publications · 3.5K citations
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19 publications · 4.8K citations
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TRR · 17 publications · 992 citations
16
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10 publications · 380 citations
11
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9 publications · 449 citations
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