Long-term Traffic Forecasting
Long-Term Traffic Forecasting is a traffic demand forecasting method that predicts traffic conditions over extended temporal horizons, typically ranging from several hours to multiple days or weeks, distinguishing itself from short- or medium-term forecasting by its focus on capturing macroscopic patterns, trends, and structural changes in transportation networks over time.
116
Publications
10.8K
Citations
414
Authors
179
Institutions
Publications per year
2017–2026
78
Parents
116
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Haiyang Yu, Zhihai Wu, Shuqin Wang et al. · Sensors · 2017 · 618 citations · Full text
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi et al. · arXiv (Cornell University) · 2017 · 504 citations · Full text
Computing, Data Science, Forecasting +15
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179
Beijing Jiaotong University
Beijing, China
9 h-index
32 publications
3.3K citations
Tsinghua University
Beijing, China
6 h-index
15 publications
1K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
7 publications
432 citations

Beihang University
Beijing, China
4 h-index
17 publications
12.2K citations

Harbin Institute of Technology
Harbin, China
3 h-index
9 publications
434 citations

Beijing University of Posts and Telecommunications
Beijing, China
3 h-index
10 publications
249 citations

University of Minnesota
Minneapolis, United States
3 h-index
4 publications
194 citations

Beijing University of Technology
Beijing, China
2 h-index
4 publications
894 citations
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10 publications · 1K citations
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Repository · 5 publications · 1K citations
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5 publications · 226 citations
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TRR · 4 publications · 107 citations
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4 publications · 854 citations
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