Periodic Shift and Event-aware Spatio-Temporal Graph Convolutional Network for Traffic Congestion Prediction-Reference-Cited by-同舟云学术

Periodic Shift and Event-aware Spatio-Temporal Graph Convolutional Network for Traffic Congestion Prediction

Published:2023-11-13 Issue: Volume: Page:
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Container-title:Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
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Author:

Li Fuxian¹^ORCID,Yan Huan¹^ORCID,Sui Hongjie¹^ORCID,Wang Deng²^ORCID,Zuo Fan²^ORCID,Liu Yue²^ORCID,Li Yong¹^ORCID,Jin Depeng¹^ORCID

Affiliation:

1. Department of Electronic Engineering, Tsinghua University, Beijing, China

2. Alibaba Group, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589132.3625612

Reference48 articles.

1. A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System

2. Forecasting Traffic Congestion Using ARIMA Modeling

3. Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting.

4. Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks

5. Cen Chen , Kenli Li , Sin G Teo , Xiaofeng Zou , Keqin Li , and Zeng Zeng . 2020. Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks. ACM Transactions on Knowledge Discovery from Data (TKDD) 14, 4 ( 2020 ), 1--23. Cen Chen, Kenli Li, Sin G Teo, Xiaofeng Zou, Keqin Li, and Zeng Zeng. 2020. Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks. ACM Transactions on Knowledge Discovery from Data (TKDD) 14, 4 (2020), 1--23.

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