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References (25)
-
J Long, R Zhang, Z Yang, Y Huang, Y Liu, C Li (2022)
Self-adaptation graph attention network via meta-learning for machinery fault diagnosis with few labeled dataIEEE Transactions on Instrumentation and Measurement, 71
-
C Zheng, X Fan, C Wang, J Qi (2020)
GMAN: A graph multi-attention network for traffic predictionProceedings of the AAAI Conference on Artificial Intelligence, 34
-
Junbo Zhang, Yu Zheng, Dekang Qi (2016)
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction -
Hua Wei, Guanjie Zheng, Huaxiu Yao, Z. Li (2018)
IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light ControlProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
-
S. Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan (2019)
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting -
Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo (2020)
Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction -
Chao Huang, Junbo Zhang, Yu Zheng, N. Chawla (2018)
DeepCrime: Attentive Hierarchical Recurrent Networks for Crime PredictionProceedings of the 27th ACM International Conference on Information and Knowledge Management
-
BM Williams, LA Hoel (2003)
Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical resultsJournal of transportation engineering, 129
-
Siteng Huang, Donglin Wang, Xuehan Wu, Ao Tang (2019)
DSANet: Dual Self-Attention Network for Multivariate Time Series ForecastingProceedings of the 28th ACM International Conference on Information and Knowledge Management
-
Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, Shaoyao He (2019)
Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting -
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh (2019)
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
-
Can Li, Lei Bai, Wei Liu, Lina Yao, S. Waller (2020)
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural NetworkProceedings of the 29th ACM International Conference on Information & Knowledge Management
-
Chao Song, Youfang Lin, S. Guo, Huaiyu Wan (2020)
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting -
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, C. Aggarwal, P. Mitra, Suhang Wang (2019)
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing ValuesArXiv, abs/1911.10273
-
Bilong Shen, Xiaodan Liang, Yufeng Ouyang, Miaofeng Liu, Weimin Zheng, Kathleen Carley (2018)
StepDeep: A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural NetworkProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
-
ZW Hamilton, Leskovec J Ying (2017)
Inductive representation learning on large graphsIn Advances in Neural Information Processing Systems, 34
-
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi (2019)
GMAN: A Graph Multi-Attention Network for Traffic PredictionArXiv, abs/1911.08415
-
Sijie Yan, Yuanjun Xiong, Dahua Lin (2018)
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition -
Hongjian Wang, Z. Li (2017)
Region Representation Learning via Mobility FlowProceedings of the 2017 ACM on Conference on Information and Knowledge Management
-
Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, C. Yang (2019)
Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting -
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Z. Li (2018)
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction -
Ting Yu, Haoteng Yin, Zhanxing Zhu (2017)
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic ForecastingArXiv, abs/1709.04875
-
Chao Chen, K. Petty, A. Skabardonis, P. Varaiya, Zhanfeng Jia (2001)
Freeway Performance Measurement System: Mining Loop Detector DataTransportation Research Record, 1748
-
A Borovykh, S Bohte, CW Oosterlee (2017)
Conditional time series forecasting with convolutional neural networksLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10614
-
Bo Zhao, Xianmin Zhang, Qiqiang Wu, Zhuobo Yang, Zhenhui Zhan
A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machinesMechanical Systems and Signal Processing
- Publisher
- Springer Journals
- Copyright
- Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
- ISSN
- 0924-669X
- eISSN
- 1573-7497
- DOI
- 10.1007/s10489-023-04621-5
- Publisher site
- See Article on Publisher Site
Abstract
Traffic forecasting is a typical spatio-temporal graph modeling problem, which has become one of the key technical issues in modern intelligent transportation systems. However, existing methods cannot capture the long-range spatial and temporal characteristics very well because of the complexity and heterogeneity of the traffic flows. In this paper, a new deep learning framework called Graph Convolutional Dynamic Recurrent Network with Attention (GCDRNA) is proposed to predict the traffic state in the traffic network. GCDRNA mainly consists of two components, which are Graph Convolutional with Attention (GCA) block and Dynamic GRU with Attention (DGRUA) block. GCA block can capture both global and local spatial correlations of the traffic flows by k-hop GC, similarity GC and spatial attention modules. DGRUA block captures the long-term temporal correlation of the traffic flows by Dynamic GRU (DGRU) and Node Attention Unit (NAU) modules. Experimental results show that GCDRNA achieves the best prediction performance compared with other baseline models on two public real-world traffic datasets.
Journal
Applied Intelligence – Springer Journals
Published: Oct 1, 2023
Keywords: Graph convolutional network; Dynamic gate recurrent unit; Attention mechanism; Traffic flow forecasting