Traffic Flow Prediction via Spatial Temporal Graph Neural Network
Open accessAbstract
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.
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Spatial GNN models assume that the underlying structure of the data sources from which the MTS dataset is generated is known.
“Spatial GNN models (e.g., refs.[66][67][68]) are based on the assumption that the underlying structure of the data sources from which the MTS dataset is generated is known.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [68]
Tracking locations and analysing mobility has contributed to many day-to-day applications such as traffic management, routing, recommender systems, and cellular network load balancing.
“Tracking locations and analysing mobility has contributed to many day-to-day applications, for example, traffic management and forecasting [87,88], routing individuals [89] and delivery packages [90], recommender systems [91], dynamic load balancing systems for cellular networks [92], to name a few.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [87]
Cites
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
New Introduction to Multiple Time Series Analysis
Lütkepohl, H. (2005) · Springer eBooks · doi:10.1007/978-3-540-27752-1 · Cited by 6,081 · see citing works
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 · see citing works
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 · see citing works
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 3,358 · see citing works
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 · see citing works
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654 · see citing works
The Structure and Dynamics of Networks
Newman, M., Barabási, A.-Ĺ., & Watts, D. J. (2011) · Princeton University Press eBooks · doi:10.1515/9781400841356 · Cited by 2,310 · see citing works
Using LSTM and GRU neural network methods for traffic flow prediction
Fu, R., Zhang, Z., & Li, L. (2016) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2016.7804912 · Cited by 1,555 · see citing works
Recurrent neural networks and robust time series prediction
Connor, J. T., Martin, R. D., & Atlas, L. E. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279188 · Cited by 1,316 · see citing works
Big data and its technical challenges.
Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., & Shahabi, C. (2014) · Communications of the ACM · doi:10.1145/2611567 · Cited by 1,048 · see citing works
Information diffusion in online social networks
Guille, A., Hacid, H., Favre, C., & Zighed, A. D. (2013) · ACM SIGMOD Record · doi:10.1145/2503792.2503797 · Cited by 851 · see citing works
New Introduction to Multiple Time Series Analysis
Lütkepohl, H. (2005) · Springer eBooks · doi:10.1007/3-540-27752-8 · Cited by 850 · see citing works
Short-term traffic flow prediction using seasonal ARIMA model with limited input data
Kumar, S. V., & Vanajakshi, L. (2015) · European Transport Research Review · doi:10.1007/s12544-015-0170-8 · Cited by 847 · see citing works
Real-time freeway traffic state estimation based on extended Kalman filter: a general approach
Wang, Y., & Papageorgiou, M. (2004) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2004.03.003 · Cited by 805 · see citing works
Self-citationFreeway Performance Measurement System: Mining Loop Detector Data
Chen, C., Petty, K. F., Skabardonis, A., Varaiya, P. P., & Jia, Z. (2001) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1748-12 · Cited by 759 · see citing works
Lippi, M., Bertini, M., & Frasconi, P. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2247040 · Cited by 747 · see citing works
Network Representation Learning: A Survey
Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714 · see citing works
End-to-End Dense Video Captioning with Masked Transformer
Zhou, L., Zhou, Y., Corso, J. J., Socher, R., & Xiong, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00911 · Cited by 605 · see citing works
Generating Wikipedia by Summarizing Long SequencesPreprint
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, Ł., & Shazeer, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.10198 · Cited by 578 · see citing works
Diffusion-Convolutional Neural Networks
Atwood, J. R., & Towsley, D. (2016) · neural information processing systems · Cited by 566 · see citing works
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting
Yu, R., Li, Y., Shahabi, C., Demiryurek, U., & Liu, Y. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.87 · Cited by 427 · see citing works
Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction
Liu, H., Tian, H.-Q., & Li, Y. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.04.001 · Cited by 410 · see citing works
Survey on traffic prediction in smart cities
Nagy, A. M., & Simon, V. V. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.07.004 · Cited by 318 · see citing works
Traffic Flow Prediction using Kalman Filtering Technique
Kumar, S. V. (2017) · Procedia Engineering · doi:10.1016/j.proeng.2017.04.417 · Cited by 258 · see citing works
Community Detection in Attributed Graphs: An Embedding Approach
Li, Y., Sha, C., Huang, X. S., & Zhang, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11274 · Cited by 239 · see citing works
On six degrees of separation in DBLP-DB and more
Elmacioglu, E., & Lee, D. (2005) · ACM SIGMOD Record · doi:10.1145/1083784.1083791 · Cited by 152 · see citing works
Social recommendation across multiple relational domains
Jiang, M., Cui, P., Wang, F., Yang, Q., Zhu, W., & Yang, S. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398448 · Cited by 77 · see citing works
Multi-recurrent networks for traffic forecasting
Ulbricht, C. (1994) · Cited by 30 · see citing works
Flagged first, then by citations
1 of the 33 references couldn’t be matched to a known paper.
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Li, M., & Zhu, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16542 · Cited by 1,005 · see citing works
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
Jiang, J., Han, C., Zhao, W. X., & Wang, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25556 · Cited by 706 · see citing works
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Jin, G., Liang, Y., Fang, Y., Shao, Z., Huang, J., Zhang, J., & Zheng, Y. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3333824 · Cited by 643 · see citing works
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Li, F., Feng, J., Yan, H., Jin, G., Yang, F., Sun, F., Jin, D., & Li, Y. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532611 · Cited by 585 · see citing works
Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567 · see citing works
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
Shao, Z., Zhang, Z., Wei, W., Wang, F., Xu, Y., Cao, X., & Jensen, C. S. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3551793.3551827 · Cited by 456 · see citing works
Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Liu, H., Dong, Z., Jiang, R., Deng, J., Deng, J., Chen, Q., & Song, X. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615160 · Cited by 453 · see citing works
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 397 · see citing works
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Jiang, R., Wang, Z., Yong, J., Jeph, P., Chen, Q., Kobayashi, Y., Song, X., Fukushima, S., & Suzumura, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.25976 · Cited by 339 · see citing works
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Shao, Z., Zhang, Z., Wang, F., & Xu, Y. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539396 · Cited by 338 · see citing works
Shaygan, M., Meese, C., Li, W., Zhao, X., & Nejad, M. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103921 · Cited by 292 · see citing works
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Khoshraftar, S., & An, A. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3633518 · Cited by 246 · see citing works
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Zhang, X., Huang, C., Xu, Y., Xia, L., Dai, P., Bo, L., Zhang, J., & Zheng, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17761 · Cited by 236 · see citing works
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Ma, Y., Guo, Z., Ren, Z., Tang, J., & Yin, D. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401092 · Cited by 232 · see citing works
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Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 209 · see citing works
UrbanGPT: Spatio-Temporal Large Language Models
Li, Z., Xia, L., Tang, J., Xu, Y., Shi, L., Xia, L., Yin, D., & Huang, C. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671578 · Cited by 207 · see citing works
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You, J., Du, T., & Leskovec, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539300 · Cited by 194 · see citing works
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Jiang, R., Yin, D., Wang, Z., Wang, Y., Deng, J., Liu, H., Cai, Z., Deng, J., Song, X., & Shibasaki, R. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482000 · Cited by 184 · see citing works
Self-citationHuo, G., Zhang, Y., Wang, B., Gao, J., Hu, Y., & Yin, B. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3234512 · Cited by 182 · see citing works
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Chen, C., Liu, Y., Chen, L., & Chengqi Zhang, B. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183903 · Cited by 178 · see citing works
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Zheng, Y., Yi, L., & Wei, Z. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-3853-2 · Cited by 174 · see citing works
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Cirstea, R.-G., Yang, B., Guo, C., Kieu, T., & Pan, S. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00262 · Cited by 170 · see citing works
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Self-citationDiao, C., Zhang, D., Liang, W., Li, K., Hong, Y., & Gaudiot, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3140229 · Cited by 121 · see citing works
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Jiang, W., Liu, B., Liang, Y., Gao, H., Lin, P., Zhang, D., & Hu, G. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122155 · Cited by 106 · see citing works
STGNN-TTE: Travel time estimation via spatial–temporal graph neural network
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Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184 · Cited by 103 · see citing works
Self-citationRen, Q., Yang, L., & Liu, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120203 · Cited by 103 · see citing works
Bearing Remaining Useful Life Prediction Based on Regression Shapalet and Graph Neural Network
Yang, X., Zheng, Y., Zhang, Y., Wong, D. S., & Yang, W. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3151169 · Cited by 100 · see citing works
Spatio-temporal fusion graph convolutional network for traffic flow forecasting
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Enhancement of traffic forecasting through graph neural network-based information fusion techniques
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Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.09.018 · Cited by 95 · see citing works
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Xing, L., & Perelman, L. (2022) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001550 · Cited by 79 · see citing works
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He, Y., Huang, P., Hong, W., Luo, Q., Li, L., & Tsui, K. (2024) · Algorithms · doi:10.3390/a17090398 · Cited by 79 · see citing works
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Pan, Y. A., Li, F., Li, A., Niu, Z., & Liu, Z. (2025) · Multimodal Transportation · doi:10.1016/j.multra.2025.100207 · Cited by 77 · see citing works
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STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting
Liu, J., Kang, Y., Li, H., Wang, H., & Yang, X. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04122-x · Cited by 69 · see citing works
PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction
Fan, J., Weng, W., Chen, Q., Wu, H., & Wu, J. X. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106941 · Cited by 66 · see citing works
Huang, J., Luo, K., Cao, L., Wen, Y., & Zhong, S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3173689 · Cited by 64 · see citing works
Deep multi-view graph-based network for citywide ride-hailing demand prediction
Jin, G., Xi, Z., Sha, H., Feng, Y., & Huang, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.010 · Cited by 64 · see citing works
Zhang, X., Liu, J., Gong, P., Chen, C., Han, B., & Wu, Z. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115886 · Cited by 64 · see citing works
Liu, Z., Ding, F., Dai, Y., Li, L., Chen, T., & Tan, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123543 · Cited by 64 · see citing works
STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction
Wei, Z., Zhao, H., Li, Z., Bu, X., Chen, Y., Zhang, X., Lv, Y., & Wang, F. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123033 · Cited by 63 · see citing works
Adaptive Graph Fusion Convolutional Recurrent Network for Traffic Forecasting
Xu, Y., Lu, Y., Ji, C., & Zhang, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3244182 · Cited by 63 · see citing works
Zhao, S., Dai, G., Li, J., Zhu, X., Huang, X., Li, Y., Tan, M., Wang, L., Fang, P., Chen, X., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00983-9 · Cited by 62 · see citing works
Semantics-Aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting
Liang, G., KinTak, U., Ning, X., Tiwari, P., Nowaczyk, S., & Kumar, N. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3239054 · Cited by 61 · see citing works
Node Connection Strength Matrix-Based Graph Convolution Network for Traffic Flow Prediction
Chen, J., Wang, W. W., Yu, K., Hu, X., Cai, M., & Guizani, M. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3265300 · Cited by 61 · see citing works
Yang, S., Wu, Q., Wang, Y., & Zhou, Z. M. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.110046 · Cited by 61 · see citing works
Self-citationLong-Short Term Spatio-Temporal Aggregation for Trajectory Prediction
Yang, C., & Pei, Z. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3234962 · Cited by 60 · see citing works
Industry classification based on supply chain network information using Graph Neural Networks
Wu, D. D., Wang, Q., & Olson, D. L. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109849 · Cited by 59 · see citing works
GNN at the Edge: Cost-Efficient Graph Neural Network Processing Over Distributed Edge Servers
Zeng, L., Yang, C., Huang, P., Zhou, Z., Yu, S., & Chen, X. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3229422 · Cited by 58 · see citing works
3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction
Xia, T., Lin, J., Li, Y., Feng, J., Hui, P., Sun, F., Guo, D., & Jin, D. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3451394 · Cited by 57 · see citing works
Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning
Li, R., Zhong, T., Jiang, X., Trajcevski, G., Wu, J., & Zhou, F. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539422 · Cited by 56 · see citing works
Multi-range bidirectional mask graph convolution based GRU networks for traffic prediction
Hu, N., Zhang, D., Xie, K., Liang, W., Diao, C., & Li, K. (2022) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2022.102775 · Cited by 56 · see citing works
Tiansuan Constellation: An Open Research Platform
Wang, S., Li, Q., Xu, M., Ma, X., Zhou, A., & Sun, Q. (2021) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge53862.2021.00022 · Cited by 55 · see citing works
Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction
Qiao, J., Lin, Y., Bi, J., Yuan, H., Wang, G., & Zhou, M. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3285253 · Cited by 55 · see citing works
Xiao, Y., Xia, K., Yin, H., Zhang, Y., Qian, Z., Liu, Z., Liang, Y., & Li, X. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.06.019 · Cited by 53 · see citing works
A trend graph attention network for traffic prediction
Wang, C., Tian, R., Hu, J., & Ma, Z. (2022) · Information Sciences · doi:10.1016/j.ins.2022.12.048 · Cited by 53 · see citing works
Bhatti, M. A., Song, Z., Bhatti, U. A., & Syam, M. S. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00598-9 · Cited by 53 · see citing works
Efficient Graph Deep Learning in TensorFlow with tf_geometric
Hu, J., Qian, S., Fang, Q., Wang, Y., Zhao, Q., Zhang, H., & Xu, C. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3478322 · Cited by 53 · see citing works
Self-citationAIST: An Interpretable Attention-Based Deep Learning Model for Crime Prediction
Rayhan, Y., & Hashem, T. (2023) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3582274 · Cited by 51 · see citing works
A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting
Li, G., Zhong, S., Deng, X., Xiang, L., Chan, S.-H. G., Li, R., Liu, Y., Zhang, M. X., Hung, C., & Peng, W.-C. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3233086 · Cited by 51 · see citing works
Heterogeneous Temporal Graph Transformer
Fan, Y., Ju, M., Hou, S., Ye, Y., Wan, W., Wang, K., Mei, Y., & Xiong, Q. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467168 · Cited by 50 · see citing works
Spatial dynamic graph convolutional network for traffic flow forecasting
Li, H., Yang, S., Song, Y., Luo, Y., Li, J., & Zhou, T. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04271-z · Cited by 50 · see citing works
Multi-scale spatial-temporal aware transformer for traffic prediction
Tian, R., Wang, C., Hu, J., & Ma, Z. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119557 · Cited by 50 · see citing works
MTEGCRN: Multi-scale temporal enhanced graph convolutional recurrent network for traffic prediction
Yang, S., & Wu, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131064 · Cited by 50 · see citing works
Meng, C., Song, S., Tong, H., Pan, M., & Yu, Y. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00038 · Cited by 49 · see citing works
Khalili, M. A., Guerriero, L., Pouralizadeh, M., Calcaterra, D., & Martire, D. D. (2023) · Natural Hazards · doi:10.1007/s11069-023-06121-8 · Cited by 48 · see citing works
Dual attentive graph neural network for metro passenger flow prediction
Lu, Y., Ding, H., Ji, S., Sze, N. N., & He, Z. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05966-z · Cited by 48 · see citing works
Yin, N., Shen, L., Xiong, H., Gu, B., Chen, C., Hua, X., Liu, S., & Luo, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331389 · Cited by 47 · see citing works
A Spatiotemporal Multiscale Graph Convolutional Network for Traffic Flow Prediction
Cao, S., Wu, L., Zhang, R., Wu, D., Cui, J., & Chang, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3354802 · Cited by 47 · see citing works
- 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)
- Venue
- Proceedings of The Web Conference 2020
- Publisher
- ACM
- Published
- 2020-04-20
- Pages
- 1082-1092
- OpenAlex
- W3012562343
- Cited by
- 644
- References
- 33
- Citations by year
- 2026: 42 · 2025: 141 · 2024: 145 · 2023: 134 · 2022: 113 · 2021: 65 · 2020: 3 · 2017: 1
- FWCI
- 131.32
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Building and Construction › 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
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