TY - GEN
T1 - Attention-Based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
AU - Liu, Haiyang
AU - Zhu, Chunjiang
AU - Zhang, Detian
AU - Li, Qing
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023/11
Y1 - 2023/11
N2 - Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. The key challenge is to effectively model complex spatial-temporal dependencies and correlations in modern traffic data. Existing methods, however, cannot accurately model both long-term and short-term temporal correlations simultaneously, limiting their expressive power on complex spatial-temporal patterns. In this paper, we propose a novel spatial-temporal neural network framework: Attention-based Spatial-Temporal Graph Convolutional Recurrent Network (ASTGCRN), which consists of a graph convolutional recurrent module (GCRN) and a global attention module. In particular, GCRN integrates gated recurrent units and adaptive graph convolutional networks for dynamically learning graph structures and capturing spatial dependencies and local temporal relationships. To effectively extract global temporal dependencies, we design a temporal attention layer and implement it as three independent modules based on multi-head self-attention, transformer, and informer respectively. Extensive experiments on five real traffic datasets have demonstrated the excellent predictive performance of all our three models with all their average MAE, RMSE and MAPE across the test datasets lower than the baseline methods.
AB - Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. The key challenge is to effectively model complex spatial-temporal dependencies and correlations in modern traffic data. Existing methods, however, cannot accurately model both long-term and short-term temporal correlations simultaneously, limiting their expressive power on complex spatial-temporal patterns. In this paper, we propose a novel spatial-temporal neural network framework: Attention-based Spatial-Temporal Graph Convolutional Recurrent Network (ASTGCRN), which consists of a graph convolutional recurrent module (GCRN) and a global attention module. In particular, GCRN integrates gated recurrent units and adaptive graph convolutional networks for dynamically learning graph structures and capturing spatial dependencies and local temporal relationships. To effectively extract global temporal dependencies, we design a temporal attention layer and implement it as three independent modules based on multi-head self-attention, transformer, and informer respectively. Extensive experiments on five real traffic datasets have demonstrated the excellent predictive performance of all our three models with all their average MAE, RMSE and MAPE across the test datasets lower than the baseline methods.
KW - Attention mechanism
KW - Graph convolutional networks
KW - Traffic forecasting
UR - http://www.scopus.com/inward/record.url?scp=85177046762&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-46661-8_42
DO - 10.1007/978-3-031-46661-8_42
M3 - Conference article published in proceeding or book
AN - SCOPUS:85177046762
SN - 9783031466601
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 630
EP - 645
BT - Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings
A2 - Yang, Xiaochun
A2 - Wang, Bin
A2 - Suhartanto, Heru
A2 - Wang, Guoren
A2 - Jiang, Jing
A2 - Li, Bing
A2 - Zhu, Huaijie
A2 - Cui, Ningning
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Advanced Data Mining and Applications, ADMA 2023
Y2 - 21 August 2023 through 23 August 2023
ER -