Cited by 651Traffic Prediction and Management TechniquesTraffic control and managementAdvanced Graph Neural Networks

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Open access

Wang, Xiaoyang, Ma, Yao, Wang, Yiqi, Jin, Wei, Wang, Xin, Tang, Jiliang, Jia, Caiyan, Yu, Jian·2020·Proceedings of The Web Conference 2020

DOI 10.1145/3366423.3380186

Open this paper in CoChat — free account

Abstract

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.

Integrity signals

Registry

Crossref · 10.1145/3366423.3380186

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
New Introduction to Multiple Time Series Analysis
Lütkepohl, H. (2005) · Springer eBooks · doi:10.1007/978-3-540-27752-1 · Cited by 5,972
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410
Time series forecasting using a hybrid ARIMA and neural network model
Zhang, G. P. (2003) · Neurocomputing · doi:10.1016/s0925-2312(01)00702-0 · Cited by 4,576
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Zhao, L. D., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & Li, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2935152 · Cited by 3,529
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000

Show all 33 references

Research works printed but not linked: 17

Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450(2016). Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450(2016). · arXiv
Albert-Laszlo Barabasi . 2003 . Linked: How everything is connected to everything else and what it means. Plume. Albert-Laszlo Barabasi. 2003. Linked: How everything is connected to everything else and what it means. Plume.
Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203(2013). Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203(2013). · arXiv
10.5555/3015812.3015982 · DOI
Ennio Cascetta . 2013. Transportation systems engineering: theory and methods. Vol. 49 . Springer Science & Business Media . Ennio Cascetta. 2013. Transportation systems engineering: theory and methods. Vol. 49. Springer Science & Business Media.
Kyunghyun Cho Bart Van Merriënboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014). Kyunghyun Cho Bart Van Merriënboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014). · arXiv
William L Hamilton Rex Ying and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584(2017). William L Hamilton Rex Ying and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584(2017). · arXiv
Sepp Hochreiter and Jürgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735–1780. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016). · arXiv
Yann LeCun , Yoshua Bengio , 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10(1995) , 1995 . Yann LeCun, Yoshua Bengio, 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361, 10(1995), 1995.
Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926(2017). Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926(2017). · arXiv
Niki Parmar Ashish Vaswani Jakob Uszkoreit Łukasz Kaiser Noam Shazeer Alexander Ku and Dustin Tran. 2018. Image transformer. arXiv preprint arXiv:1802.05751(2018). Niki Parmar Ashish Vaswani Jakob Uszkoreit Łukasz Kaiser Noam Shazeer Alexander Ku and Dustin Tran. 2018. Image transformer. arXiv preprint arXiv:1802.05751(2018). · arXiv
Alex J Smola and Bernhard Schölkopf . 2004. A tutorial on support vector regression. Statistics and computing 14, 3 ( 2004 ), 199–222. Alex J Smola and Bernhard Schölkopf. 2004. A tutorial on support vector regression. Statistics and computing 14, 3 (2004), 199–222.
Théo Trouillon , Johannes Welbl , Sebastian Riedel , Éric Gaussier , and Guillaume Bouchard . 2016 . Complex embeddings for simple link prediction . In International Conference on Machine Learning. 2071–2080 . Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In International Conference on Machine Learning. 2071–2080.
Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Łukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Łukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.
Wan Kamarul Ariffin Wan Ahmad and Sabri Ahmad . 2013 . Arima model and exponential smoothing method: A comparison . In AIP Conference Proceedings, Vol. 1522 . AIP, 1312–1321. Wan Kamarul Ariffin Wan Ahmad and Sabri Ahmad. 2013. Arima model and exponential smoothing method: A comparison. In AIP Conference Proceedings, Vol. 1522. AIP, 1312–1321.
Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , and Dit-Yan Yeung . 2018 . Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294(2018). Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018. Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294(2018). · arXiv

Sorted by citations

Items 101–200 of 651

Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
Zhang, H., Zhu, Y., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3392782 · Cited by 47
Graph Neural Network for Spatiotemporal Data: Methods and ApplicationsPreprint
Li, Y., Yu, D., Liu, Z., Zhang, M., Gong, X., & Zhao, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4725185 · Cited by 57
A Comprehensive Survey of Deep Learning-Based Traffic Flow Prediction Models for Intelligent Transportation Systems
Ali, R., Ali, A., Naeem, H. M. Y., Asad, M., Alsarhan, T., & Heyat, M. B. B. (2024) · ICCK Transactions on Advanced Computing and Systems · doi:10.62762/tacs.2024.795448 · Cited by 48
Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction
Ouyang, X., Yang, Y. Z., Zhang, Y., Zhou, W., Wan, J., & Du, S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110885 · Cited by 49
Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction
Hu, N., Zhang, D., Xie, K., Liang, W., Li, K., & Zomaya, A. Y. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.052 · Cited by 47
A Survey on Exploring Real and Virtual Social Network Rumors: State-of-the-Art and Research Challenges
He, Q., Zhang, S., Cai, Y., Yuan, W., Ma, L., & Yu, K. (2025) · ACM Computing Surveys · doi:10.1145/3716498 · Cited by 48
Comparison of biomedical relationship extraction methods and models for knowledge graph creation
Milošević, N., & Thielemann, W. (2022) · Journal of Web Semantics · doi:10.1016/j.websem.2022.100756 · Cited by 49
Open Spatio-Temporal Foundation Models for Traffic Prediction
Li, Z., Xia, L., Shi, L., Xu, Y., Yin, D., & Huang, C. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3773912 · Cited by 73
AutoGRN: An adaptive multi-channel graph recurrent joint optimization network with Copula-based dependency modeling for spatio-temporal fusion in electrical power systems
Wang, H., Qiu, X., Xiong, Y.-J., & Tan, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102836 · Cited by 43
Spatio-Temporal Missing Data Imputation for Smart Power Grids
Kuppannagari, S. R., Fu, Y., Cheung, C. M., & Prasanna, V. K. (2021) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3447555.3466586 · Cited by 42
DSTGCN: Dynamic Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Hu, J., Lin, X., & Wang, C. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3176016 · Cited by 43
DAGCRN: Graph convolutional recurrent network for traffic forecasting with dynamic adjacency matrix
Shi, Z., Zhang, Y., Wang, J., Qin, J., Liu, X., Yin, H., & Huang, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120259 · Cited by 45
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Wu, Z., Zheng, D., Pan, S., Gan, Q., Long, G., & Karypis, G. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3186103 · Cited by 45
FASTNN: A Deep Learning Approach for Traffic Flow Prediction Considering Spatiotemporal Features
Zhou, Q., Chen, N., & Lin, S. (2022) · Sensors · doi:10.3390/s22186921 · Cited by 43
Federated deep learning for smart city edge-based applications
Djenouri, Y., Michalak, T., & Lin, J. C. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.04.034 · Cited by 41
Sparse Mobile Crowdsensing for Cost-Effective Traffic State Estimation With Spatio–Temporal Transformer Graph Neural Network
Xue, J., Xu, Y., Wu, W. R., Zhang, T., Shen, Q., Zhou, H., & Zhuang, W. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3356554 · Cited by 45
Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting
Liang, G., Tiwari, P., Nowaczyk, S., Byttner, S., & Alonso‐Fernandez, F. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3415149 · Cited by 47
PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction
Wang, T., Ngoduy, D., Zou, G., Dantsuji, T., Liu, Z. A., & Li, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125144 · Cited by 45
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classification
Liu, M., Liang, K., Hu, D., Yu, H., Liu, Y., Meng, L., Tu, W., Zhou, S., & Liu, X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611853 · Cited by 40
Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Luo, X., Zhu, C., Zhang, D., & Li, Q. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230446 · Cited by 45
Graph Time-series Modeling in Deep Learning: A Survey
Chen, H., & Eldardiry, H. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3638534 · Cited by 43
Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network
Lu, B., Gan, X., Jin, H., Fu, L., Wang, X., & Zhang, H. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3488902 · Cited by 40
Self-citation
Full-scale spatio-temporal traffic flow estimation for city-wide networks: a transfer learning based approach
Zhang, Y., Cheng, Q., Liu, Y., & Liu, Z. (2022) · Transportmetrica B Transport Dynamics · doi:10.1080/21680566.2022.2143453 · Cited by 39
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Wu, X., Zhang, D., Zhang, M., Guo, C., Yang, B., & Jensen, C. S. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3588951 · Cited by 42
DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting
Zheng, Q., & Zhang, Y. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3156366 · Cited by 40
IG-Net: An Interaction Graph Network Model for Metro Passenger Flow Forecasting
Liu, Z., Wang, S., Zhao, H., Yu, J. X., Hu, L., Yin, H., & Liu, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235805 · Cited by 38
Self-citation
Monitoring industrial control systems via spatio-temporal graph neural networks
Wang, Y., Peng, H., Wang, G., Tang, X., Wang, X., & Liu, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106144 · Cited by 38
Self-citation
Traffic Prediction With Transfer Learning: A Mutual Information-Based Approach
Yun-jie, H., Song, X., Zhu, Y., Zhang, S., & Yu, J. J. Q. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3266398 · Cited by 39
Self-citation
Attention-Based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting
Liu, H., Zhu, C., Zhang, D., & Li, Q. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46661-8_42 · Cited by 41
A Survey of Graph-Based Resource Management in Wireless Networks—Part II: Learning Approaches
Dai, Y., Lyu, L., Cheng, N., Sheng, M., Liu, J., Wang, X., Cui, S. R., Cai, L., & Shen, X. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3508777 · Cited by 37
Self-citation
A Survey of Traffic Flow Prediction Methods Based on Long Short-Term Memory Networks
Ye, B., Zhang, M., Li, L., Liu, C., & Wu, W. (2024) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2024.3400679 · Cited by 43
Spatio-Temporal Graph Convolutional Networks via View Fusion for Trajectory Data Analytics
Hu, W., Li, W., Zhou, X., Kawai, A., Fueda, K., Qian, Q., & Wang, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3210559 · Cited by 36
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
Zheng, C., Fan, X., Wang, C., Qi, J., Chen, C., & Chen, L. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583525 · Cited by 35
ODMixer: Fine-Grained Spatial-Temporal MLP for Metro Origin-Destination Prediction
Liu, Y., Chen, B., Zheng, Y., Cheng, L., Li, G., & Lin, L. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3579370 · Cited by 38
Few-shot Node Classification on Attributed Networks with Graph Meta-learning
Liu, Y., Li, M., Li, X., Giunchiglia, F., Feng, X., & Guan, R. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531978 · Cited by 36
ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios
Qu, Y., Rong, J., Li, Z., & Chen, K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110591 · Cited by 35
Towards hour-level crime prediction: A neural attentive framework with spatial–temporal-categorical fusion
Liang, W., Wang, Y., Tao, H., & Cao, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.052 · Cited by 34
Self-citation
ST-GAT
Song, J., Son, J., Seo, D.-H., Han, K., Kim, N., & Kim, S. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557705 · Cited by 34
Spatio-temporal envolutional graph neural network for traffic flow prediction in UAV-based urban traffic monitoring system
Ma, W., Chu, Z., Chen, H., & Li, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-78335-0 · Cited by 37
DAHGN: Degree-Aware Heterogeneous Graph Neural Network
Zhao, M., & Jia, A. L. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111355 · Cited by 33
Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification
Qiang, Y., Dong, X., Liu, X., Yang, Y., Fang, Y., & Dou, J. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108406 · Cited by 36
BA-GNN: On Learning Bias-Aware Graph Neural Network
Chen, Z., Xiao, T., & Kuang, K. (2022) · IEEE International Conference on Data Engineering · doi:10.1109/icde53745.2022.00271 · Cited by 49
Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on Discrete Wavelet Transformation
Li, H., Lv, Z., Li, J., Xu, Z., Wang, Y., Sun, H., & Sheng, Z. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3564753 · Cited by 34
Self-citation
Spatio-Temporal Transformer Network for Weather Forecasting
Ji, J., He, J., Lei, M., Wang, M., & Tang, W. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3378061 · Cited by 33
Long-term spatio-temporal graph attention network for traffic forecasting
Remmouche, B., Boukraà, D., Zakharova, A., Bouwmans, T., & Taffar, M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128244 · Cited by 32
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
Liu, Z., Luo, Y., Wu, L., Li, S., Liu, Z., & Li, S. Z. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557238 · Cited by 32
Spatio-temporal Dual Graph Neural Networks for Travel Time Estimation
Jin, G., Yan, H., Li, F., Huang, J., & Li, Y. (2023) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3627819 · Cited by 32
LCDFormer: Long-term correlations dual-graph transformer for traffic forecasting
Cai, J., Wang, C., & Hu, K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123721 · Cited by 32
Landslide Displacement Prediction via Attentive Graph Neural Network
Kuang, P., Li, R., Huang, Y., Wu, J., Luo, X., & Zhou, F. (2022) · Remote Sensing · doi:10.3390/rs14081919 · Cited by 32
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30
Adaptive Decision Spatio-temporal neural ODE for traffic flow forecasting with Multi-Kernel Temporal Dynamic Dilation Convolution
Chu, Z., Ma, W., Li, M., & Chen, H. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106549 · Cited by 31
GE-STDGN: a novel spatio-temporal weather prediction model based on graph evolution
Ni, Q., Wang, Y., & Fang, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02824-2 · Cited by 32
Self-citation
A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction
Li, G., Wang, X., Njoo, G. S., Zhong, S., Chan, S.-H. G., Hung, C., & Peng, W.-C. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00058 · Cited by 30
Self-citation
Dual Graph Convolution Architecture Search for Travel Time Estimation
Jin, G., Yan, H., Li, F., Li, Y., & Huang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3591361 · Cited by 31
A review of graph theory-based diagnosis of neurological disorders based on EEG and MRI
Yan, Y., Liu, G., Cai, H., Wu, E. Q., Cai, J., Cheok, A. D., Liu, N., Li, T., & Fan, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128098 · Cited by 29
Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting
Yang, S., Li, H., Luo, Y., Li, J., Song, Y., & Zhou, T. (2022) · Mathematics · doi:10.3390/math10091594 · Cited by 29
ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning
Li, Y., Yuan, H., Fu, Z., Ma, X., Xu, M., & Wang, S. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583436 · Cited by 30
Deep learning on multi-view sequential data: a survey
Xie, Z., Yang, Y. Z., Zhang, Y., Wang, J., & Du, S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10332-z · Cited by 28
Runoff Prediction Based on Dynamic Spatiotemporal Graph Neural Network
Yang, S., Zhang, Y., & Zhang, Z. (2023) · Water · doi:10.3390/w15132463 · Cited by 30
Dynamic multi-granularity spatial-temporal graph attention network for traffic forecasting
Sang, W., Zhang, H., Kang, X., Nie, P., Meng, X., Boulet, B., & Sun, P. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120230 · Cited by 29
A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory
Wan, F., & Li, P. (2024) · Symmetry · doi:10.3390/sym16030378 · Cited by 31
Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis
Feng, C., Liu, C., & Jiang, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111838 · Cited by 30
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control with Spatio-Temporal Attention Mechanism
Jia, W., & Ji, M. (2025) · Applied Sciences · doi:10.3390/app15158605 · Cited by 28
Multi-spatio-temporal Fusion Graph Recurrent Network for Traffic Forecasting
Zhao, W. G., Zhang, S., Zhou, B., & Wang, B. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106615 · Cited by 29
Adaptive graph neural network for traffic flow prediction considering time variation
Chen, F., Sun, X., Wang, Y., Xu, Z., & Ma, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124430 · Cited by 28
Self-citation
SGCNAX: A Scalable Graph Convolutional Neural Network Accelerator with Workload Balancing
Li, J., Zheng, H., Wang, K., & Louri, A. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3133691 · Cited by 26
MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation
Zhang, J., Luo, D., & Wei, H. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599435 · Cited by 26
Influenza-like symptom recognition using mobile sensing and graph neural networks
Dong, G., Cai, L., Datta, D., Kumar, S., Barnes, L. E., & Boukhechba, M. (2021) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3450439.3451880 · Cited by 25
Identifying Critical Links in Urban Transportation Networks Based on Spatio-Temporal Dependency Learning
Xinlong, H., Hu, S., Wang, W. W., Kaparias, I., Zhong, S., Na, X., Bell, M. G. H., & Lee, D. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3339507 · Cited by 25
InsGNN: Interpretable spatio-temporal graph neural networks via information bottleneck
Fang, H., Wang, H., Gao, Y., Zhang, Y., Bu, J., Han, B., & Lin, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102997 · Cited by 24
Modeling Network-level Traffic Flow Transitions on Sparse Data
Lei, X., Mei, H., Shi, B., & Wei, H. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539236 · Cited by 24
Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference
Dai, S., Wang, J., Huang, C., Yu, Y., & Dong, J. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3564754 · Cited by 26
Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster
Jiang, R., Wang, Z., Tao, Y., Yang, C., Song, X., Shibasaki, R., Chen, S., & Shyu, M. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583991 · Cited by 25
MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation
An, J., Gao, M. Y., & Tang, J. (2024) · ACM Transactions on Information Systems · doi:10.1145/3664651 · Cited by 23
Self-citation
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Hu, J., Liu, X., Fan, Z., Liang, Y., & Zimmermann, R. (2024) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3678717.3691235 · Cited by 29
Conflict forecasting with event data and spatio-temporal graph convolutional networks
Brandt, P. T., D'Orazio, V. J., Khan, L. R., Li, Y., Osorio, J., & Sianan, M. (2022) · International Interactions · doi:10.1080/03050629.2022.2036987 · Cited by 25
Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship Between Flows
Zhao, Y., Lin, Y., Zhang, Y., Wen, H., Liu, Y., Wu, H., Wu, Z., Zhang, S., & Wan, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3187121 · Cited by 23
An Adaptive Federated Relevance Framework for Spatial–Temporal Graph Learning
Zhang, T., Liu, Y., Shen, Z., Xu, R., Chen, X., Xiao-wei, H., & Zheng, X. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3316629 · Cited by 23
Spatio-temporal graph attention networks for traffic prediction
Ma, C., Li, Y., & Xu, G. (2023) · Transportation Letters · doi:10.1080/19427867.2023.2261706 · Cited by 23
DPEM: Dual-Perspective Enhanced Mamba for multivariate time series forecasting
Hou, R. L., Liu, Q., He, P., Liu, Y., Huang, Y., Xie, J., Xie, Y., & Dai, T. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103250 · Cited by 22
Fograph: Enabling Real-Time Deep Graph Inference with Fog Computing
Zeng, L., Huang, P., Luo, K., Zhang, X., Zhou, Z., & Chen, X. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511982 · Cited by 23
One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial–Temporal Dependency
Luo, G., Zhang, H., Yuan, Q., Li, J., Wang, W., & Wang, F. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3259045 · Cited by 22
Hierarchical Multiview Top-k Pooling With Deep-Q-Networks
Li, Z., Su, H., Wu, Y., Zhang, Q., Yuan, C., Gribova, V. V., Filaretov, V. F., & Huang, D.-S. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3334261 · Cited by 22
A graph-based approach for traffic prediction using similarity and causal relations between nodes
Khaled, A., Elsir, A. M. T., Wang, P., Shen, Y., & Zhang, Q. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111913 · Cited by 22
Deep spatio-temporal feature fusion learning for multi-step building cooling load forecasting
Zou, M., Huang, W., Jin, J., Hu, B., & Liu, Z. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114735 · Cited by 23
The prediction of contact force networks in granular materials based on graph neural networks
Li, Z., Li, X., Zhang, H., Huang, D., & Zhang, L. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0122695 · Cited by 21
Towards real-world traffic prediction and data imputation: A multi-task pretraining and fine-tuning approach
Qu, Y., Li, Z., Zhao, X., & Ou, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119972 · Cited by 22
Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis
Zeng, X., Zhou, T., Bao, Z., Zhao, H., Chen, L., Wang, X., & Wang, F. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3230987 · Cited by 20
Self-citation
An adaptive structural dominant failure modes searching method based on graph neural network
Tian, Y., Guan, X., Sun, H., & Bao, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109841 · Cited by 20
Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations
Liang, K., Zhou, S., Liu, M., Liu, Y., Tu, W., Zhang, Y., Fang, L., Liu, Z., & Liu, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28719 · Cited by 20
Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems
Zaraket, K., Harb, H., Bennis, I., Jaber, A. H., & Abouaissa, A. (2024) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.102954 · Cited by 20
Deep Generative Models for Spatial Networks
Guo, X., Du, Y., & Zhao, L. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467394 · Cited by 20
CS-TGN: Community Search via Temporal Graph Neural Networks
Hashemi, F., Behrouz, A., & Hajidehi, M. R. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587654 · Cited by 20
CCDSReFormer: Traffic flow prediction with a criss-crossed dual-stream enhanced rectified transformer model
Shao, Z., Bell, M. G. H., Wang, Z., Geers, G., Yao, X., & Gao, J. (2025) · Communications in Transportation Research · doi:10.1016/j.commtr.2025.100189 · Cited by 22
Backbone-based Dynamic Spatio-Temporal Graph Neural Network for epidemic forecasting
Mao, J., Han, Y., Tanaka, G., & Wang, B. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111952 · Cited by 19
Spatiotemporal information conversion machine for time-series forecasting
Peng, H., Chen, P., Liu, R., & Chen, L. (2022) · Fundamental Research · doi:10.1016/j.fmre.2022.12.009 · Cited by 18
Multi-Task Weakly Supervised Learning for Origin–Destination Travel Time Estimation
Wang, H., Zhang, Z., Fan, Z., Chen, J., Zhang, L., Shibasaki, R., & Song, X. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3236060 · Cited by 19
BiLSTM‐ and GNN‐Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data
Alourani, A., Ashfaq, F., Jhanjhi, N. Z., & Khan, N. A. (2023) · Journal of Advanced Transportation · doi:10.1155/2023/8962283 · Cited by 22
Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems
Adewopo, V. A., Elsayed, N., Elsayed, Z., Özer, M., Zekios, C. L., Abdelgawad, A., & Bayoumi, M. (2024) · IEEE SoutheastCon · doi:10.1109/southeastcon52093.2024.10500288 · Cited by 18
DyExplainer: Self-explainable Dynamic Graph Neural Network with Sparse Attentions
Wang, T., Luo, D., Cheng, W. D., Chen, H., & Zhang, X. D. (2025) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3729173 · Cited by 19

Authors: Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J.
Affiliations: Beijing Jiaotong University (CN)
Michigan State University (US)
ORCID: Wang, Xiaoyang
Ma, Yao
Wang, Yiqi
Jin, Wei
Wang, Xin
Tang, Jiliang
Jia, Caiyan
Yu, Jian
Venue: Proceedings of The Web Conference 2020
Publisher: ACM
Published: 2020-04-20
Pages: 1082-1092
DOI: 10.1145/3366423.3380186
OpenAlex: W3012562343
Cited by: 644
References: 33
Citations by year: 2026: 42 · 2025: 141 · 2024: 145 · 2023: 134 · 2022: 113 · 2021: 65 · 2020: 3
FWCI: 131.32
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: International World Wide Web Conference (WWW) (publishedVersion, cc-by)
Topics: Social Sciences › Transportation › Traffic Prediction and Management Techniques
Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Keywords: traffic flow prediction, spatio-temporal graph neural networks, spatial dependence, temporal dependencies, traffic dynamics, smart cities, transportation networks

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks