FEST: A Multi-way Framework with Enhanced Spatial-Temporal Modeling for Traffic Forecasting
Author:
Affiliation:
1. Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China
2. Independent Researcher, Beijing, China
3. Institute of Software, Chinese Academy of Sciences, Beijing, China
Funder
CAS Project for Young Scientists in Basic Research
Youth Innovation Promotion Association CAS
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3652583.3658111
Reference33 articles.
1. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer Normalization. arxiv: 1607.06450 [stat.ML]
2. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815.
3. Chao Chen, Karl Petty, and Alex Skabardonis. 2000. Freeway performance measurement: Mining loop detector data. Transportation Research Record Journal of the Transportation Research Board 1748 (2000).
4. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
5. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting
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