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This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

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GNN4Traffic

This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

If you find this repository helpful, you may consider cite our relevant work:

  • Jiang W, Luo J. Graph Neural Network for Traffic Forecasting: A Survey[J]. arXiv preprint arXiv:2101.11174, 2021. Link
  • Jiang W, Luo J. Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools[J]. arXiv preprint arXiv:2103.11824, 2021. Link

For a wider collection of deep learning for traffic forecasting, you may check: DL4Traffic

Advertisement: If you are interested in maintaining this repository, feel free to drop me an email. Collaboration with Tsinghua University is always welcome, too.

Some simple paper statistics results are as follows.

Paper year count:

Top conferences with paper counts:

Top journals with paper counts:

Relevant Repositories

  • Deep Learning Time Series Forecasting Link

  • A collection of research on spatio-temporal data mining Link

  • Some TrafficFlowForecasting Solutions Link

  • Urban-computing-papers Link

  • Spatio-Temporal-papers Link

  • Awesome-Mobility-Machine-Learning-Contents Link

  • Traffic Prediction Link

2022

Journal

  • Jin G, Wang M, Zhang J, et al. STGNN-TTE: Travel time estimation via spatial–temporal graph neural network[J]. Future Generation Computer Systems, 2022, 126: 70-81. Link

2021

Journal

  • Xia T, Lin J, Li Y, et al. 3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021, 15(6): 1-21. Link Code

  • Zhang H, Chen L, Cao J, et al. A Combined Traffic Flow Forecasting Model Based on Graph Convolutional Network and Attention Mechanism[J]. International Journal of Modern Physics C, 2021. Link

  • Zhang Z, Lin X, Li M, et al. A customized deep learning approach to integrate network-scale online traffic data imputation and prediction[J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103372. Link

  • Xu M, Liu H. A flexible deep learning-aware framework for travel time prediction considering traffic event[J]. Engineering Applications of Artificial Intelligence, 2021, 106: 104491. Link

  • Zhang T, Ding W, Chen T, et al. A Graph Convolutional Method for Traffic Flow Prediction in Highway Network[J]. Wireless Communications and Mobile Computing, 2021, 2021. Link

  • Chen P, Fu X, Wang X. A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Zhang S, Guo Y, Zhao P, et al. A Graph-Based Temporal Attention Framework for Multi-Sensor Traffic Flow Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link data

  • Han Y, Peng T, Wang C, et al. A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow[J]. ISPRS International Journal of Geo-Information, 2021, 10(4): 222. Link

  • Chen L, Bei L, An Y, et al. A Hyperparameters automatic optimization method of time graph convolution network model for traffic prediction[J]. Wireless Networks, 2021: 1-9. Link

  • Feng S, Ke J, Yang H, et al. A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Chen K, Deng M, Shi Y. A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data[J]. ISPRS International Journal of Geo-Information, 2021, 10(9): 624. Link

  • Chen Z, Xu J, Lin Y, et al. A Traffic Flow Forecasting Method Regarding Traffic Network as an Digraph[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2021: 2159043. Link

  • Kong X, Zhang J, Wei X, et al. Adaptive spatial-temporal graph attention networks for traffic flow forecasting[J]. Applied Intelligence, 2021: 1-17. Link

  • Hu Z, Sun R, Shao F, et al. An Efficient Short-Term Traffic Speed Prediction Model Based on Improved TCN and GCN[J]. Sensors, 2021, 21(20): 6735. Link Data

  • Zhu J, Tao C, Deng H, et al. AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting[J]. IEEE Access. Link Code

  • Buroni G, Lebichot B, Bontempi G. AST-MTL: An Attention-based Multi-Task Learning Strategy for Traffic Forecasting[J]. IEEE Access, 2021. Link Code

  • Ye J, Xue S. Attention-based spatio-temporal graph convolutional network considering external factors for multistep traffic flow prediction[J]. Digital Communications and Networks, 2021. Link

  • Jiang H, Li L, Xian H, et al. Crowd Flow Prediction for Social Internet-of-Things Systems Based on the Mobile Network Big Data[J]. IEEE Transactions on Computational Social Systems, 2021. Link

  • Pan C, Zhu J, Kong Z, et al. DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting[J]. Electronics, 2021, 10(9): 1014. Link

  • Bai L, Yao L, Wang X, et al. Deep spatial-temporal sequence modeling for multi-step passenger demand prediction[J]. Future Generation Computer Systems, 2021. Link

  • Lu Y, Ding H, Ji S, et al. Dual attentive graph neural network for metro passenger flow prediction[J]. Neural Computing and Applications, 2021: 1-15. Link

  • Yang T, Tang X, Liu R. Dual temporal gated multi-graph convolution network for taxi demand prediction[J]. Neural Computing and Applications, 2021: 1-16. Link

  • Peng H, Du B, Liu M, et al. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning[J]. Information Sciences, 2021, 578: 401-416. Link

  • Liu Z, Liu Z, Fu X. Dynamic Origin-Destination Flow Prediction Using Spatial-Temporal Graph Convolution Network With Mobile Phone Data[J]. IEEE Intelligent Transportation Systems Magazine, 2021. Link

  • Cho J H, Ham S W, Kim D K. Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems using a Graph Convolutional Network with Public Transit Usage Data[J]. Transportation Research Record, 2021: 03611981211012003. Link

  • Zhang C, Zhang S, James J Q, et al. FASTGNN: A Topological Information Protected Federated Learning Approach For Traffic Speed Forecasting[J]. IEEE Transactions on Industrial Informatics, 2021. Link

  • Yang X, Zhu Q, Li P, et al. Fine-grained predicting urban crowd flows with adaptive spatio-temporal graph convolutional network[J]. Neurocomputing, 2021, 446: 95-105. Link

  • Fang M, Tang L, Yang X, et al. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Wang X, Chai Y, Li H, et al. Graph Convolutional Network-based Model for Incident-related Congestion Prediction: A Case Study of Shanghai Expressways[J]. ACM Transactions on Management Information Systems (TMIS), 2021, 12(3): 1-22. Link

  • Liu D, Xu X, Xu W, et al. Graph convolutional network: traffic speed prediction fused with traffic flow data[J]. Sensors, 2021, 21(19): 6402. Link

  • Wang Q, Xu C, Zhang W, et al. GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs[J]. IEEE Signal Processing Letters, 2021. Link

  • Jin C, Ruan T, Wu D, et al. HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction[J]. Journal of Ambient Intelligence and Humanized Computing, 2021. Link

  • An J, Guo L, Liu W, et al. IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction[J]. Neural Networks, 2021. Link

  • Ke J, Feng S, Zhu Z, et al. Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach[J]. Transportation Research Part C: Emerging Technologies, 2021, 127: 103063. Link

  • Dong H, Ding F, Tan H, et al. Laplacian integration of graph convolutional network with tensor completion for traffic prediction with missing data in inter-city highway network[J]. Physica A: Statistical Mechanics and its Applications, 2021: 126474. Link

  • Guo S, Lin Y, Wan H, et al. Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. Link

  • Zou X, Zhang S, Zhang C, et al. Long-term Origin-Destination Demand Prediction with Graph Deep Learning[J]. IEEE Transactions on Big Data, 2021. Link

  • James J Q, Markos C, Zhang S. Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Fang Z, Pan L, Chen L, et al. MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data[J]. Proc. VLDB Endow., 2021, 14(8): 1289-1297. Link

  • Zhao D, Ju C, Zhu G, et al. MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Wang J, Zhang Y, Wei Y, et al. Metro Passenger Flow Prediction via Dynamic Hypergraph Convolution Networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link Code (still empty on 2021/10/17)

  • Sun B, Zhao D, Shi X, et al. Modeling Global Spatial–Temporal Graph Attention Network for Traffic Prediction[J]. IEEE Access, 2021. Link

  • Tang J, Liang J, Liu F, et al. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network[J]. Transportation Research Part C: Emerging Technologies, 2021, 124: 102951. Link

  • Zhang Z, Li Y, Song H, et al. Multiple dynamic graph based traffic speed prediction method[J]. Neurocomputing, 2021, 461: 109-117. Link

  • Li G, Knoop V L, van Lint H. Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations[J]. Transportation Research Part C: Emerging Technologies, 2021, 128: 103185. Link Code

  • Wu X, Fang J, Liu Z, et al. Multistep Traffic Speed Prediction from Spatial–Temporal Dependencies Using Graph Neural Networks[J]. Journal of Transportation Engineering, Part A: Systems, 2021, 147(12): 04021082. Link

  • Fang S, Prinet V, Chang J, et al. MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Wang F, Xu J, Liu C, et al. On prediction of traffic flows in smart cities: a multitask deep learning based approach[J]. World Wide Web, 2021: 1-19. Link

  • Liu M, Li L, Li Q, et al. Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network[J]. ISPRS International Journal of Geo-Information, 2021, 10(7): 455. Link

  • Ke J, Qin X, Yang H, et al. Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network[J]. Transportation Research Part C: Emerging Technologies, 2021, 122: 102858. Link Code

  • Li M, Gao S, Lu F, et al. Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks[J]. International Journal of Geographical Information Science, 2021: 1-28. Link data and code

  • Yang J M, Peng Z R, Lin L. Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization[J]. Transportation Research Part C: Emerging Technologies, 2021, 129: 103228. Link Code

  • Jiang M, Chen W, Li X. S-GCN-GRU-NN: A novel hybrid model by combining a Spatiotemporal Graph Convolutional Network and a Gated Recurrent Units Neural Network for short-term traffic speed forecasting[J]. Journal of Data, Information and Management, 1-20. Link

  • Agafonov A A. Short-Term Traffic Data Forecasting: A Deep Learning Approach[J]. Optical Memory and Neural Networks, 2021, 30(1): 1-10. Link Code

  • Tian C, Chan W K. Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies[J]. IET Intelligent Transport Systems, 2021. Link Code

  • Yan B, Wang G, Yu J, et al. Spatial-Temporal Chebyshev Graph Neural Network for Traffic Flow Prediction in IoT-based ITS[J]. IEEE Internet of Things Journal, 2021. Link

  • Bui K H N, Cho J, Yi H. Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues[J]. Applied Intelligence, 2021: 1-12. Link

  • Luo D, Zhao D, Ke Q, et al. Spatio-Temporal Hashing Multi-Graph Convolutional Network for Service-level Passenger Flow Forecasting in Bus Transit Systems[J]. IEEE Internet of Things Journal, 2021. Link

  • Li D, Lasenby J. Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. Link

  • Li X, Wang H, Sun P, et al. Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model[J]. Sustainability 2021, 13, 1253. Link

  • Zhang S, Chen Y, Zhang W. Spatiotemporal fuzzy-graph convolutional network model with dynamic feature encoding for traffic forecasting[J]. Knowledge-Based Systems, 2021: 107403. Link

  • Tang J, Zeng J. Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data[J]. Computer‐Aided Civil and Infrastructure Engineering, 2021. Link

  • Zhu K, Zhang S, Li J, et al. Spatiotemporal multi-graph convolutional networks with synthetic data for traffic volume forecasting[J]. Expert Systems with Applications, 2021: 115992. Link

  • Jin G, Wang M, Zhang J, et al. STGNN-TTE: Travel time estimation via spatial–temporal graph neural network[J]. Future Generation Computer Systems, 2022, 126: 70-81. Link

  • Zi W, Xiong W, Chen H, et al. TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network[J]. Information Sciences, 2021, 561: 274-285. Link

  • Zhang J, Chen H, Fang Y. TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data[J]. Journal of Electrical and Computer Engineering, 2021, 2021. Link

  • Kwak S, Geroliminis N, Frossard P. Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs[J]. IEEE Transactions on Signal and Information Processing over Networks, 2021. Link Code

  • Xu C, Zhang A, Xu C, et al. Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features[J]. Applied Intelligence, 2021: 1-19. Link Data

Conference

  • Chen Z, Wu H, O'Connor N E, et al. A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes[C]. 2021 IEEE 24rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2021. Link

  • Yi P, Huang F, Peng J. A Fine-grained Graph-based Spatiotemporal Network for Bike Flow Prediction in Bike-sharing Systems[C]//Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2021: 513-521. Link

  • Li B, Guo T, Wang Y, et al. Adaptive Graph Co-Attention Networks for Traffic Forecasting[C]//PAKDD (1). 2021: 263-276. Link

  • Wu W, Fan X, Xue Y, et al. An attention mechanism-based method for predicting traffic flow by GCN[C]//2021 40th Chinese Control Conference (CCC). IEEE, 2021: 8410-8415. Link

  • Lee H, Park C, Jin S, et al. An Empirical Experiment on Deep Learning Models for Predicting Traffic Data[C]. Accepted at 37th IEEE International Conference on Data Engineering (ICDE 2021), 2021. Link

  • Yang G, Li Y, Zhou W, et al. Attention Mechanism Based on Temporal Graph Convolutional Neural Network for Traffic Flow Prediction[C]//Proceedings of 2021 Chinese Intelligent Systems Conference. Springer, Singapore, 2022: 434-442. Link

  • Jiang R, Wang Z, Cai Z, et al. Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2021: 319-334. Link Code

  • Ye J, Sun L, Du B, et al. Coupled Layer-wise Graph Convolution for Transportation Demand Prediction[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. Link Code

  • Meng C, Rambhatla S, Liu Y. Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling[C]. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '21). Association for Computing Machinery. 2021. Link

  • Shang C, Chen J, Bi J. Discrete Graph Structure Learning for Forecasting Multiple Time Series[C]. International Conference on Learning Representations (ICLR), 2021. Link Code

  • He C, Wang H, Jiang X, et al. Dyna-PTM: OD-enhanced GCN for Metro Passenger Flow Prediction[C]//2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021: 1-9. Link

  • Liu H, Zhang F, Fan Y, et al. Enhanced Self-node Weights Based Graph Convolutional Networks for Passenger Flow Prediction[C]//International Conference on Knowledge Science, Engineering and Management. Springer, Cham, 2021: 262-274. Link

  • Cirstea R G, Kieu T, Guo C, et al. EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting[C]//2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021: 1739-1750. Link

  • Derrow-Pinion A, She J, Wong D, et al. ETA Prediction with Graph Neural Networks in Google Maps[C]. CIKM, 2021. Link

  • Oreshkin B N, Amini A, Coyle L, et al. FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. Link Code

  • Wang Z, Xia T, Jiang R, et al. Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks[C]//2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021: 1751-1762. Link

  • Wang Y, Yin H, Chen T, et al. Gallat: A Spatiotemporal Graph Attention Network for Passenger Demand Prediction[C]//2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021: 2129-2134. Link

  • Dees B S, Xu Y L, Constantinides A G, et al. Graph Theory for Metro Traffic Modelling[C]. International Joint Conference on Neural Networks (IJCNN), 2021. Link

  • Zhong W, Suo Q, Jia X, et al. Heterogeneous Spatio-Temporal Graph ConvolutionNetwork for Traffic Forecasting with Missing Values[C]//2021 IEEE 41th International Conference on Distributed Computing Systems (ICDCS). IEEE. 2021. Link

  • Guo K, Hu Y, Sun Y, et al. Hierarchical Graph Convolution Networks for Traffic Forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. Link Code

  • He B, Li S, Zhang C, et al. Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2021: 321-336. Link

  • Huang J, Chen L, An Y, et al. Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction[C]//International Conference on Simulation Tools and Techniques. Springer, Cham, 2020: 681-693. Link

  • Zhan Q, Wu G, Gan C. MAGCN: A Multi-Adaptive Graph Convolutional Network for Traffic Forecasting[C]//2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021: 1-8. Link Code

  • Wang S, Zhang M, Miao H, et al. MT-STNets: Multi-Task Spatial-Temporal Networks for Multi-Scale Traffic Prediction[C]//Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2021: 504-512. Link

  • Hu J, Chen L. Multi-Attention Based Spatial-Temporal Graph Convolution Networks for Traffic Flow Forecasting[C]//2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021: 1-7. Link

  • Jing B, Tong H, Zhu Y. Network of Tensor Time Series[C]. Accepted by WWW 2021. Link

  • Lin H, Fan Y, Zhang J, et al. REST: Reciprocal Framework for Spatiotemporal-coupled Predictions[C]//Proceedings of the Web Conference 2021. 2021: 3136-3145. Link

  • Pal S, Ma L, Zhang Y, et al. RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting[C]. Accepted at the International Conference on Machine Learning (ICML) 2021. Link Code

  • Yang G, Wen J, Yu D, et al. Spatial-Temporal Dilated and Graph Convolutional Network for traffic prediction[C]//2020 Chinese Automation Congress (CAC). IEEE, 2020: 802-806. Link

  • Mengzhang L, Zhanxing Z. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. Link Code

  • Fang Z, Long Q, Song G, et al. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting[C]. In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '21). Association for Computing Machinery. 2021. Link Code

  • Hong G, Wang Z, Han T, et al. Spatiotemporal Multi-Graph Convolutional Network for Taxi Demand Prediction[C]//2021 11th International Conference on Information Science and Technology (ICIST). IEEE, 2021: 242-250. Link

  • Roy A, Roy K K, Ali A A, et al. SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network[C]. Accepted for publication in 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021). Link

  • Zhu M, Zhu X, Zhu C. STGATP: A Spatio-Temporal Graph Attention Network for Long-Term Traffic Prediction[C]//International Conference on Artificial Neural Networks. Springer, Cham, 2021: 255-266. Link

  • Liu Z, Ding Z, Yang B, et al. ST-GWANN: A Novel Spatial-Temporal Graph Wavelet Attention Neural Network for Traffic Prediction[C]//International Conference on Spatial Data and Intelligence. Springer, Cham, 2021: 83-99. Link

  • Bu X, Wei Z, Li Z, et al. Temporal-Difference Spatial Sampling and Aggregating Graph Neural Network for Crowd Flow Forecasting[C]//2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2021: 160-163. Link

  • Fu H, Wang Z, Yu Y, et al. Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-Hailing Demand Forecasting[C]//PAKDD (1). 2021: 754-765. Link

  • Zhang X, Huang C, Xu Y, Xia L, et al. Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. Link Code

  • Xiao W, Kuang L, An Y. Traffic Flow Prediction Through the Fusion of Spatial-Temporal Data and Points of Interest[C]//International Conference on Database and Expert Systems Applications. Springer, Cham, 2021: 314-327. Link Code

  • Li M, Tong P, Li M, et al. Traffic Flow Prediction with Vehicle Trajectories[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. Link Code

  • Yao X, Zhang Z, Cui R, et al. Traffic Prediction Based on Multi-graph Spatio-Temporal Convolutional Network[C]//International Conference on Web Information Systems and Applications. Springer, Cham, 2021: 144-155. Link

  • Yang Q, Zhong T, Zhou F. Traffic Speed Forecasting Via Spatio-Temporal Attentive Graph Isomorphism Network[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 7943-7947. Link

  • Chen X, Wang J, Xie K. TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI. 2021. Link Code

  • Hui B, Yan D, Chen H, et al. TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021: 716-724. Link

  • Zhang W, Zhang C, Tsung F. Transformer Based Spatial-Temporal Fusion Network for Metro Passenger Flow Forecasting[C]//2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). IEEE, 2021: 1515-1520. Link

  • Roy A, Roy K K, Ali A A, et al. Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network[C]. 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. Link Code

  • Chen Y, Segovia-Dominguez I, Gel Y R. Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting[C]. Accepted at the International Conference on Machine Learning (ICML) 2021. Link Code

Book Chapter

  • Xu D, Dai H, Xuan Q. Graph Convolutional Recurrent Neural Networks: A Deep Learning Framework for Traffic Prediction[M]//Graph Data Mining. Springer, Singapore, 2021: 189-204. Link

Preprint

  • Fu J, Zhou W, Chen Z. Bayesian Graph Convolutional Network for Traffic Prediction[J]. arXiv preprint arXiv:2104.00488, 2021. Link

  • Lin H, Gao Z, Wu L, et al. Conditional Local Filters with Explainers for Spatio-Temporal Forecasting[J]. arXiv preprint arXiv:2101.01000, 2021. Link

  • Hu J, Liang Y, Fan Z, et al. Decoupling Long-and Short-Term Patterns in Spatiotemporal Inference[J]. arXiv preprint arXiv:2109.09506, 2021. Link

  • Li F, Feng J, Yan H, et al. Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution[J]. arXiv preprint arXiv:2104.14917, 2021. Link Code

  • Chen J, Li K, Li K, et al. Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network[J]. arXiv preprint arXiv:2101.07425, 2021. Link

  • Li Y, Wang D, Moura J M F. GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention[J]. arXiv preprint arXiv:2104.05914, 2021. Link

  • Ye J, Zheng F, Zhao J, et al. Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting[J]. arXiv preprint arXiv:2107.01528, 2021. Link

  • Grigsby J, Wang Z, Qi Y. Long-Range Transformers for Dynamic Spatiotemporal Forecasting[J]. arXiv preprint arXiv:2109.12218, 2021. Link

  • Xu Y, Liu W, Jiang Z, et al. MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting[J]. arXiv preprint arXiv:2108.03594, 2021. Link

  • He Y, Li L, Zhu X, et al. Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow[J]. arXiv preprint arXiv:2107.13226, 2021. Link

  • Ye J, Zheng F, Zhao J, et al. Multi-View TRGRU: Transformer based Spatiotemporal Model for Short-Term Metro Origin-Destination Matrix Prediction[J]. arXiv preprint arXiv:2108.03900, 2021. Link Code

  • Li M, Chen S, Shen Y, et al. Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network[J]. arXiv preprint arXiv:2107.00894, 2021. Link

  • Gao F, Wang Z, Liu Z. Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction[J]. arXiv preprint arXiv:2109.00924, 2021. Link

  • Wang Y, Yin H, Chen T, et al. Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph[J]. arXiv preprint arXiv:2101.00752, 2021. Link

  • Wang T, Zhang Z, Tsui K L. PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction[J]. arXiv preprint arXiv:2108.02424, 2021. Link

  • Jin G, Yan H, Li F, et al. Spatial-Temporal Dual Graph Neural Networks for Travel Time Estimation[J]. arXiv preprint arXiv:2105.13591, 2021. Link

  • Xu X, Zhang T, Xu C, et al. Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction[J]. arXiv preprint arXiv:2103.06126, 2021. Link

  • Huang C. STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction[J]. arXiv preprint arXiv:2107.04980, 2021. Link

  • Lu Y, Kamranfar P, Lattanzi D, et al. Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks[J]. arXiv preprint arXiv:2110.01535, 2021. Link

2020

Journal

  • Tang C, Sun J, Sun Y, et al. A General Traffic Flow Prediction Approach Based on Spatial-Temporal Graph Attention[J]. IEEE Access, 2020, 8: 153731-153741. Link Code

  • Bogaerts T, Masegosa A D, Angarita-Zapata J S, et al. A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data[J]. Transportation Research Part C: Emerging Technologies, 2020, 112: 62-77. Link

  • Qin K, Xu Y, Kang C, et al. A graph convolutional network model for evaluating potential congestion spots based on local urban built environments[J]. Transactions in GIS. Link

  • Li Z, Xiong G, Tian Y, et al. A Multi-Stream Feature Fusion Approach for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link

  • Zhang Y, Cheng T, Ren Y, et al. A novel residual graph convolution deep learning model for short-term network-based traffic forecasting[J]. International Journal of Geographical Information Science, 2020, 34(5): 969-995. Link

  • Zhu H, Xie Y, He W, et al. A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB[J]. Journal of Advanced Transportation, 2020, 2020. Link

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  • Sun J, Zhang J, Li Q, et al. Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. Link

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  • Zhang W, Liu H, Liu Y, et al. Semi-Supervised City-Wide Parking Availability Prediction via Hierarchical Recurrent Graph Neural Network[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. Link

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  • Pan Z, Zhang W, Liang Y, et al. Spatio-Temporal Meta Learning for Urban Traffic Prediction[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. Link

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  • Xu Z, Kang Y, Cao Y, et al. Spatiotemporal Graph Convolution Multifusion Network for Urban Vehicle Emission Prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020. Link

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  • Jin G, Cui Y, Zeng L, et al. Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network[J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102665. Link

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Conference

  • Li Z, Li L, Peng Y, et al. A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting[C]//2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2020: 355-362. Link

  • Zhang Y, Dong X, Shang L, et al. A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing[C]//2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2020: 1-9. Link

  • Bai L, Yao L, Li C, et al. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting[C]//Advances in Neural Information Processing Systems (NeurIPS), 2020. Link Code

  • Lu Y, Li C. AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. Link Code

  • Zhao H, Yang H, Wang Y, et al. Attention Based Graph Bi-LSTM Networks for Traffic Forecasting[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020: 1-6. Link

  • Zhang H, Liu J, Tang Y, et al. Attention based Graph Covolution Networks for Intelligent Traffic Flow Analysis[C]//2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020: 558-563. Link

  • Wu Z, Pan S, Long G, et al. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks[C].//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020. Link Code

  • Xiaomin Fang, Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, and Haifeng Wang. 2020. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20). Association for Computing Machinery, New York, NY, USA, 2697–2705. Link

  • Sun Y, Wang Y, Fu K, et al. Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting[C]. International Conference on Pattern Recognition. Springer, 2020. Link

  • Zhang X, Cao R, Zhang Z, et al. Crowd Flow Forecasting with Multi-Graph Neural Networks[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-7. Link

  • Xie Q, Guo T, Chen Y, et al. Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM). 2020. Link Note: previously known as: " How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction[J]. Link_arxiv

  • He S, Shin K G. Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration[C]//Proceedings of The Web Conference 2020. 2020: 133-143. Link

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  • Tang C, Sun J, Sun Y. Dynamic Spatial-Temporal Graph Attention Graph Convolutional Network for Short-Term Traffic Flow Forecasting[C]//2020 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2020: 1-5. Link

  • Ma J, Gu J, Zhou Q, et al. Dynamic-Static-based Spatiotemporal Multi-Graph Neural Networks for Passenger Flow Prediction[C]//2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2020: 673-678. Link

  • Shao K, Wang K, Chen L, et al. Estimation of Urban Travel Time with Sparse Traffic Surveillance Data[C]//Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence. 2020: 218-223. Link

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  • He Y, Zhao Y, Wang H, et al. GC-LSTM: A Deep Spatiotemporal Model for Passenger Flow Forecasting of High-Speed Rail Network[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020: 1-6. Link

  • Chen L, Han K, Yin Q, et al. GDCRN: Global Diffusion Convolutional Residual Network for Traffic Flow Prediction[C]//International Conference on Knowledge Science, Engineering and Management. Springer, Cham, 2020: 438-449. Link

  • Zheng C, Fan X, Wang C, et al. Gman: A graph multi-attention network for traffic prediction[C].//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link Code

  • Song Q, Ming R B, Hu J, et al. Graph Attention Convolutional Network: Spatiotemporal Modeling for Urban Traffic Prediction[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020: 1-6. Link Code (Still empty till 2021/10/17)

  • Chen F, Chen Z, Biswas S, et al. Graph Convolutional Networks with Kalman Filtering for Traffic Prediction[C]//Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 2020: 135-138. Link Code

  • Chen J, Liao S, Hou J, et al. GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences[C]//2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020: 1604-1609. Link

  • Huiting Hong, Yucheng Lin, Xiaoqing Yang, Zang Li, Kung Fu, Zheng Wang, Xiaohu Qie, and Jieping Ye. 2020. HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20). Association for Computing Machinery, New York, NY, USA, 2444–2454. Link Code

  • Dai R, Xu S, Gu Q, et al. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data[C].//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020. Link

  • Xin Y, Miao D, Zhu M, et al. InterNet: Multistep Traffic Forecasting by Interacting Spatial and Temporal Features[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 2020: 3477-3480. Link

  • Yeghikyan G, Opolka F L, Nanni M, et al. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks[C]//2020 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2020. Link Code

  • Huang R, Huang C, Liu Y, et al. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI. 2020. Link

  • Qu Y, Zhu Y, Zang T, et al. Modeling Local and Global Flow Aggregation for Traffic Flow Forecasting[C]//International Conference on Web Information Systems Engineering (WISE). Springer, Cham, 2020: 414-429. Link

  • Chen W, Chen L, Xie Y, et al. Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting[C].//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link

  • Ye J, Zhao J, Ye K, et al. Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. Link Code

  • Wang S, Miao H, Chen H, et al. Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 2020: 1555-1564. Link

  • Wu M, Zhu C, Chen L. Multi-Task Spatial-Temporal Graph Attention Network for Taxi Demand Prediction[C]//Proceedings of the 2020 5th International Conference on Mathematics and Artificial Intelligence. 2020: 224-228. Link

  • Wang F, Xu J, Liu C, et al. MTGCN: A Multitask Deep Learning Model for Traffic Flow Prediction[C]//International Conference on Database Systems for Advanced Applications (DASFAA). Springer, Cham, 2020: 435-451. Link

  • Shi H, Yao Q, Guo Q, et al. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network[C]//2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020: 1818-1821. Link

  • Hu J, Yang B, Guo C, et al. Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks[C]//2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020: 1417-1428. Link Code

  • Heglund J S W, Taleongpong P, Hu S, et al. Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020: 1-6. Link

  • Yang F, Chen L, Zhou F, et al. Relational State-Space Model for Stochastic Multi-Object Systems[C]//International Conference on Learning Representations. 2020. Link Code

  • Qin T, Liu T, Wu H, et al. RESGCN: RESidual Graph Convolutional Network based Free Dock Prediction in Bike Sharing System[C]//2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020: 210-217. Link

  • Zhou Z, Wang Y, Xie X, et al. RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link Code

  • Xie Y, Xiong Y, Zhu Y. SAST-GNN: A Self-Attention Based Spatio-Temporal Graph Neural Network for Traffic Prediction[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2020: 707-714. Link

  • Li W, Yang X, Tang X, et al. SDCN: Sparsity and Diversity Driven Correlation Networks for Traffic Demand Forecasting[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. Link

  • Zhang W, Liu H, Liu Y, et al. Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link Code

  • Li A, Axhausen K W. Short-term Traffic Demand Prediction using Graph Convolutional Neural Networks[C]. AGILE: GIScience Series, 2020, 1: 1-14. Link

  • Huang Y, Zhang S, Wen J, et al. Short-Term Traffic Flow Prediction Based on Graph Convolutional Network Embedded LSTM[C]//International Conference on Transportation and Development (ICTD) 2020. Reston, VA: American Society of Civil Engineers, 2020: 159-168. Link

  • Wang Q, Guo B, Ouyang Y, et al. Spatial Community-Informed Evolving Graphs for Demand Prediction[C]. Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020). Link

  • Lu B, Gan X, Jin H, et al. Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 2020: 1025-1034. Link Code

  • Zhang X, Huang C, Xu Y, et al. Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 2020: 1853-1862. Link Code

  • Zhang X, Zhang Z, Jin X. Spatial-Temporal Graph Attention Model on Traffic Forecasting[C]//2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020: 999-1003. Link

  • Wei C, Sheng J. Spatial-temporal Graph Attention Networks for Traffic Flow Forecasting[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2020, 587(1): 012065. Link

  • Song C, Lin Y, Guo S, et al. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting[C].//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link Author's Code Code1 Code2

  • Zhang Q, Chang J, Meng G, et al. Spatio-Temporal Graph Structure Learning for Traffic Forecasting[C].//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link

  • Cao D, Wang Y, Duan J, et al. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting[C]. Advances in Neural Information Processing Systems, 2020, 33. Link

  • Ou J, Sun J, Zhu Y, et al. STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 2020: 1185-1194. Link

  • Park C, Lee C, Bahng H, et al. ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). 2020: 1215-1224. Link Note: previously known as ST-GRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting[C] Link_arxiv

  • Ruiqiang Liu, Shuai Zhao, Bo Cheng, et al. ST-MFM: A Spatiotemporal Multi-Modal Fusion Model for Urban Anomalies Prediction[C]//Proceedings of the Twenty-fourth European Conference on Artificial Intelligence. 2020. Link Code (Still empty on 2021/10/17)

  • Tian K, Guo J, Ye K, et al. ST-MGAT: Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting[C]//2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2020: 714-721. Link Code

  • Li Z, Sergin N D, Yan H, et al. Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction[C].//Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Link

  • Chen L. The Multi-Task Time-Series Graph Network for Traffic Congestion Prediction[C]//2020 The 3rd International Conference on Machine Learning and Machine Intelligence. 2020: 19-23. Link

  • Suining He and Kang G. Shin. 2020. Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems. In Proceedings of The Web Conference 2020 (WWW ’20). Association for Computing Machinery, New York, NY, USA, 88–98. Link

  • Xu X, Zheng H, Feng X, et al. Traffic Flow Forecasting with Spatial-Temporal Graph Convolutional Networks in Edge-Computing Systems[C]//2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020: 251-256. Link

  • Agafonov A. Traffic Flow Prediction Using Graph Convolution Neural Networks[C]//2020 10th International Conference on Information Science and Technology (ICIST). IEEE, 2020: 91-95. Link

  • Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020. Traffic Flow Prediction via Spatial Temporal Graph Neural Network. In Proceedings of The Web Conference 2020 (WWW ’20). Association for Computing Machinery, New York, NY, USA, 1082–1092. Link

  • Ramadan A, Elbery A, Zorba N, et al. Traffic Forecasting using Temporal Line Graph Convolutional Network: Case Study[C]//ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-6. Link

  • Mallick T, Balaprakash P, Rask E, et al. Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting[C]. International Conference on Pattern Recognition. Springer, 2020. Link Code

  • Chen X, Zhang Y, Du L, et al. TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. Link

  • Kim S S, Chung M, Kim Y K. Urban Traffic Prediction using Congestion Diffusion Model[C]//2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2020: 1-4. Link

Preprint

  • Chen H, Rossi R A, Mahadik K, et al. A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting[J]. arXiv preprint arXiv:2009.12469, 2020. Link

  • Chan V, Gan Q, Bayen A. A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction[J]. arXiv preprint arXiv:2012.13479, 2020. Link Code

  • Zhu J, Song Y, Zhao L, et al. A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting[J]. arXiv preprint arXiv:2006.11583v1, 2020. Link Code

  • Wang C, Zhang K, Wang H, et al. Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results[J]. arXiv preprint arXiv:2010.07474, 2020. Link Code

  • Fu J, Zhou W, Chen Z. Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting[J]. arXiv preprint arXiv:2010.07498, 2020. Link

  • Jin G, Xi Z, Sha H, et al. Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction[J]. arXiv preprint arXiv:2007.15189, 2020. Link

  • Jia C, Wu B, Zhang X P. Dynamic Spatiotemporal Graph Neural Network with Tensor Network[J]. arXiv preprint arXiv:2003.08729, 2020. Link

  • Hermsen F, Bloem P, Jansen F, et al. End-to-End Learning from Complex Multigraphs with Latent Graph Convolutional Networks[J]. arXiv preprint arXiv:1908.05365, 2019. Link Code

  • Wang L, Chai D, Liu X, et al. Exploring the Generalizability of Spatio-Temporal Crowd Flow Prediction: Meta-Modeling and an Analytic Framework[J]. arXiv preprint arXiv:2009.09379, 2020. Link

  • Xie Y, Xiong Y, Zhu Y. ISTD-GCN: Iterative Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Speed Forecasting[J]. arXiv preprint arXiv:2008.03970, 2020. Link

  • Zhu J, Han X, Deng H, et al. KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting[J]. arXiv preprint arXiv:2011.14992, 2020. Link

  • Zheng B, Hu Q, Ming L, et al. Spatial-Temporal Demand Forecasting and Competitive Supply via Graph Convolutional Networks[J]. arXiv preprint arXiv:2009.12157, 2020. Link

  • Pian W, Wu Y. Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction[J]. arXiv preprint arXiv:2006.05905, 2020. Link

  • Xu M, Dai W, Liu C, et al. Spatial-Temporal Transformer Networks for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2001.02908, 2020. Link

  • Maas T, Bloem P. Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction[J]. arXiv preprint arXiv:2012.05207, 2020. Link

2019

Journal

  • Yang S, Ma W, Pi X, et al. A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources[J]. Transportation Research Part C: Emerging Technologies, 2019, 107: 248-265. Link

  • Zhang Y, Cheng T, Ren Y. A graph deep learning method for short‐term traffic forecasting on large road networks[J]. Computer‐Aided Civil and Infrastructure Engineering, 2019, 34(10): 877-896. Link

  • Wei L, Yu Z, Jin Z, et al. Dual Graph for Traffic Forecasting[J]. IEEE Access, 2019. Link

  • San Kim T, Lee W K, Sohn S Y. Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects[J]. PloS one, 2019, 14(9). Link

  • Xu Y, Li D. Incorporating graph attention and recurrent architectures for city-wide taxi demand prediction[J]. ISPRS International Journal of Geo-Information, 2019, 8(9): 414. Link

  • Zhu H, Luo Y, Liu Q, et al. Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph Convolutional Neural Network with Attention Mechanism[J]. International Journal of Software Engineering and Knowledge Engineering, 2019, 29(11n12): 1727-1740. Link

  • Zhang Z, Li M, Lin X, et al. Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies[J]. Transportation research part C: emerging technologies, 2019, 105: 297-322. Link

  • Han Y, Wang S, Ren Y, et al. Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks[J]. ISPRS International Journal of Geo-Information, 2019, 8(6): 243. Link

  • Yu J J Q, Gu J. Real-time traffic speed estimation with graph convolutional generative autoencoder[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3940-3951. Link

  • Xu D, Dai H, Wang Y, et al. Road traffic state prediction based on a graph embedding recurrent neural network under the SCATS[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019, 29(10): 103125. Link

  • Xie Z, Lv W, Huang S, et al. Sequential graph neural network for urban road traffic speed prediction[J]. IEEE Access, 2019. Link

  • Zhang C, James J Q, Liu Y. Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting[J]. IEEE Access, 2019, 7: 166246-166256. Link

  • Zhao L, Song Y, Zhang C, et al. T-gcn: A temporal graph convolutional network for traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2019. Link Code

  • Cui Z, Henrickson K, Ke R, et al. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2019. Link Code

Conference

  • Li Z, Xiong G, Chen Y, et al. A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 1929-1933. Link

  • Guo J, Song C, Wang H. A Multi-step Traffic Speed Forecasting Model Based on Graph Convolutional LSTM[C]//2019 Chinese Automation Congress (CAC). IEEE, 2019: 2466-2471. Link

  • Guo S, Lin Y, Feng N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 922-929. Link Code-gluon Code-pytorch Code1

  • Guo R, Jiang Z, Huang J, et al. BikeNet: Accurate Bike Demand Prediction Using Graph Neural Networks for Station Rebalancing[C]//2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2019: 686-693. Link

  • Diao Z, Wang X, Zhang D, et al. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 890-897. Link

  • Chen C, Li K, Teo S G, et al. Gated Residual Recurrent Graph Neural Networks for Traffic Prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 485-492. Link

  • Zhang Y, Wang S, Chen B, et al. GCGAN: Generative Adversarial Nets with Graph CNN for Network-Scale Traffic Prediction[C]//2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019: 1-8. Link

  • Cirstea R G, Guo C, Yang B. Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting[C]. MiLeTS’19, Anchorage, Alaska, USA, 2019. Link

  • Jepsen T S, Jensen C S, Nielsen T D. Graph convolutional networks for road networks[C]//Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2019: 460-463. Link Code

  • Wu Z, Pan S, Long G, et al. Graph wavenet for deep spatial-temporal graph modeling[C]. //Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019. Link Code

  • Fang S, Zhang Q, Meng G, et al. Gstnet: Global spatial-temporal network for traffic flow prediction[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019: 10-16. Link

  • Kang Z, Xu H, Hu J, et al. Learning Dynamic Graph Embedding for Traffic Flow Forecasting: A Graph Self-Attentive Method[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 2570-2576. Link

  • Lu Z, Lv W, Xie Z, et al. Leveraging Graph Neural Network with LSTM For Traffic Speed Prediction[C]//2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2019: 74-81. Link

  • Zhang T, Jin J, Yang H, et al. Link speed prediction for signalized urban traffic network using a hybrid deep learning approach[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 2195-2200. Link

  • Wright M A, Ehlers S F G, Horowitz R. Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 3898-3905. Link Code

  • James J Q. Online Traffic Speed Estimation for Urban Road Networks with Few Data: A Transfer Learning Approach[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019: 4024-4029. Link

  • Wang Y, Yin H, Chen H, et al. Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1227-1235. Link

  • Hasanzadeh A, Liu X, Duffield N, et al. Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction[C]//2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019: 3779-3788. Link

  • Yoshida A, Yatsushiro Y, Hata N, et al. Practical End-to-End Repositioning Algorithm for Managing Bike-Sharing System[C]//2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019: 1251-1258. Link

  • Opolka F L, Solomon A, Cangea C, et al. Spatio-temporal deep graph infomax[C]. Representation Learning on Graphs and Manifolds, ICLR 2019 Workshop. Link

  • Bai L, Yao L, Kanhere S S, et al. Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). 2019: 2293-2296. Link

  • Geng X, Li Y, Wang L, et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 3656-3663. Link

  • Bai L, Yao L, Kanhere S S, et al. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2019: 1981-1987. Link

  • Ge L, Li H, Liu J, et al. Temporal Graph Convolutional Networks for Traffic Speed Prediction Considering External Factors[C]//2019 20th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2019: 234-242. Link

  • Ge L, Li H, Liu J, et al. Traffic Speed Prediction with Missing Data Based on TGCN[C]//2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2019: 522-529. Link

  • Ren Y, Xie K. Transfer Knowledge Between Sub-regions for Traffic Prediction Using Deep Learning Method[C]//International Conference on Intelligent Data Engineering and Automated Learning. Springer, Cham, 2019: 208-219. Link

  • Pan Z, Liang Y, Wang W, et al. Urban traffic prediction from spatio-temporal data using deep meta learning[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 1720-1730. Link Code

Preprint

  • Yu B, Li M, Zhang J, et al. 3d graph convolutional networks with temporal graphs: A spatial information free framework for traffic forecasting[J]. arXiv preprint arXiv:1903.00919, 2019. Link

  • Zhang N, Guan X, Cao J, et al. A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network[J]. arXiv preprint arXiv:1904.06656, 2019. Link

  • Lee K, Rhee W. DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting[J]. arXiv preprint arXiv:1905.12256, 2019. Link

  • Lee D, Jung S, Cheon Y, et al. Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding[J]. arXiv preprint arXiv:1905.10709, 2019. Link Code

  • Lee K, Rhee W. Graph Convolutional Modules for Traffic Forecasting[J]. arXiv preprint arXiv:1905.12256, 2019. Link

  • Lu M, Zhang K, Liu H, et al. Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction[J]. arXiv preprint arXiv:1903.06261, 2019. Link

  • Shleifer S, McCreery C, Chitters V. Incrementally Improving Graph WaveNet Performance on Traffic Prediction[J]. arXiv preprint arXiv:1912.07390, 2019. Link Code

  • Geng X, Wu X, Zhang L, et al. Multi-modal graph interaction for multi-graph convolution network in urban spatiotemporal forecasting[J]. arXiv preprint arXiv:1905.11395, 2019. Link

  • Zhou X, Shen Y, Huang L. Revisiting Flow Information for Traffic Prediction[J]. arXiv preprint arXiv:1906.00560, 2019. Link

  • Yu B, Yin H, Zhu Z. ST-UNet: A spatio-temporal U-network for graph-structured time series modeling[J]. arXiv preprint arXiv:1903.05631, 2019. Link

2018

Journal

  • Lin L, He Z, Peeta S. Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach[J]. Transportation Research Part C: Emerging Technologies, 2018, 97: 258-276. Link

Conference

  • Chai D, Wang L, Yang Q. Bike flow prediction with multi-graph convolutional networks[C]//Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2018: 397-400. Link Code

  • Liao B, Zhang J, Wu C, et al. Deep sequence learning with auxiliary information for traffic prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 537-546. Link Code

  • Li Y, Yu R, Shahabi C, Liu Y, Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting[C], ICLR 2018. Link Code-tensorflow Code-pytorch

  • Zhang, J., Shi, X., Xie, J., Ma, H., King, I., & Yeung, D. (2018). GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. UAI. Link Code

  • Wu T, Chen F, Wan Y. Graph Attention LSTM Network: A New Model for Traffic Flow Forecasting[C]//2018 5th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2018: 241-245. Link

  • Wang B, Luo X, Zhang F, et al. Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data[C]. MiLeTS’18, London, United Kingdom, 2018. Link

  • Li J, Peng H, Liu L, et al. Graph CNNs for urban traffic passenger flows prediction[C]//2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2018: 29-36. Link Code

  • Mohanty S, Pozdnukhov A. Graph cnn+ lstm framework for dynamic macroscopic traffic congestion prediction[C]//International Workshop on Mining and Learning with Graphs. 2018. Link Code

  • Zhang Q, Jin Q, Chang J, et al. Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting[C]//2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018: 1018-1023. Link

  • Yu B, Yin H, Zhu Z. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI. 2018. Link Code1 Code2 Code3

Preprint

  • Wang X, Chen C, Min Y, et al. Efficient metropolitan traffic prediction based on graph recurrent neural network[J]. arXiv preprint arXiv:1811.00740, 2018. Link Code

  • Hu J, Guo C, Yang B, et al. Recurrent Multi-Graph Neural Networks for Travel Cost Prediction[J]. arXiv preprint arXiv:1811.05157, 2018. Link

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