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

• 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

• 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

• Guo K, Hu Y, Qian Z S, et al. An Optimized Temporal-Spatial Gated Graph Convolution Network for Traffic Forecasting[J]. IEEE Intelligent Transportation Systems Magazine, 2020. Link

• Luo M, Du B, Klemmer K, et al. D3P: Data-driven Demand Prediction for Fast Expanding Electric Vehicle Sharing Systems[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(1): 1-21. Link

• Xiao G, Wang R, Zhang C, et al. Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks[J]. Multimedia Tools and Applications, 2020: 1-19. Link

• Wang H W, Peng Z R, Wang D, et al. Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach[J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102619. Link Code

• Yu B, Lee Y, Sohn K. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 189-204. Link

• Ge L, Li S, Wang Y, et al. Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction[J]. Applied Sciences, 2020, 10(4): 1509. Link

• Cui Z, Ke R, Pu Z, et al. Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction[J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102620. Link

• Lu Z, Lv W, Cao Y, et al. LSTM Variants Meet Graph Neural Networks for Road Speed Prediction[J]. Neurocomputing, 2020. Link

• Guo K, Hu Y, Qian Z, et al. Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link

• Mohanty S, Pozdnukhov A, Cassidy M. Region-wide congestion prediction and control using deep learning[J]. Transportation Research Part C: Emerging Technologies, 2020, 116: 102624. Link Code

• Zhou F, Yang Q, Zhang K, et al. Reinforced Spatio-Temporal Attentive Graph Neural Networks for Traffic Forecasting[J]. IEEE Internet of Things Journal, 2020. Link

• Fukuda S, Uchida H, Fujii H, et al. Short-term Prediction of Traffic Flow under Incident Conditions using Graph Convolutional RNN and Traffic Simulation[J]. IET Intelligent Transport Systems, 2020. Link

• Peng H, Wang H, Du B, et al. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting[J]. Information Sciences, 2020, 521: 277-290. Link Code

• Zhao B, Gao X, Liu J, et al. Spatiotemporal Data Fusion in Graph Convolutional Networks for Traffic Prediction[J]. IEEE Access, 2020. Link

• Du B, Hu X, Sun L, et al. Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. 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

• 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

• 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

• 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

• Shi H, Yao Q, Guo Q, et al. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network[C]. ICDE 2020. Link (not official)

• Jilin Hu, Bin Yang, Chenjuan Guo, Christian S. Jensen, and Hui Xiong. Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks. ICDE 2020. Link Code

• 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

• 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

• 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

• 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

• Sun Y, Wang Y, Fu K, et al. Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting[J]. arXiv preprint arXiv:2004.10958, 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

• Wang X, Guan X, Cao J, et al. Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency[J]. arXiv preprint arXiv:2004.02391, 2020. Link

• Zhang J, Chen F, Guo Y. Multi-Graph Convolutional Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit[J]. arXiv preprint arXiv:2001.07512, 2020. Link

• Chen J, Liu L, Wu H, et al. Physical-Virtual Collaboration Graph Network for Station-Level Metro Ridership Prediction[J]. arXiv preprint arXiv:2001.04889, 2020. Link Code with data

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

• Mallick T, Balaprakash P, Rask E, et al. Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting[J]. arXiv preprint arXiv:2004.08038, 2020. Link Code

• 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

• 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

• 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. 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

• 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. (2020). DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting[J]. arXiv preprint arXiv:1905.12256, 2019. Link

• Zhang J, Chen F, Zhu Y, et al. Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit[J]. arXiv preprint arXiv:1912.12563, 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

• Luo M, Wen H, Luo Y, et al. Dynamic Demand Prediction for Expanding Electric Vehicle Sharing Systems: A Graph Sequence Learning Approach[J]. arXiv preprint arXiv:1903.04051, 2019. Link

• Xiong X, Ozbay K, Jin L, et al. Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter[J]. arXiv preprint arXiv:1905.00406, 2019. Link Code

• Li Y, Moura J M F. Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data[J]. arXiv preprint arXiv:1909.04019, 2019. Link

• Chandra R, Guan T, Panuganti S, et al. Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs[J]. arXiv preprint arXiv:1912.01118, 2019. Link

• 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

• Cui Z, Lin L, Pu Z, et al. Graph Markov Network for Traffic Forecasting with Missing Data[J]. arXiv preprint arXiv:1912.05457, 2019. Link

• Mallick T, Balaprakash P, Rask E, et al. Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting[J]. arXiv preprint arXiv:1909.11197, 2019. Link

• Xie Q, Guo T, Chen Y, et al. " How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction[J]. arXiv preprint arXiv:1912.01242, 2019. Link

• Shin Y Y, Yoon Y. Incorporating dynamicity of transportation network with multi-weight traffic graph convolution for traffic forecasting[J]. arXiv preprint arXiv:1909.07105, 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

• Chen W, Chen L, Xie Y, et al. Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting[J]. arXiv preprint arXiv:1911.12093, 2019. 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]. arXiv preprint arXiv:1910.09103, 2019. Link

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

• Zhang W, Liu H, Liu Y, et al. Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction[J]. arXiv preprint arXiv:1911.10516, 2019. Link

• Park C, Lee C, Bahng H, et al. STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting[J]. arXiv preprint arXiv:1911.13181, 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

• Li Z, Sergin N D, Yan H, et al. Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction[J]. arXiv preprint arXiv:1912.05693, 2019. Link

• 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

• 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

• 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

• Wang M, Lai B, Jin Z, et al. Dynamic spatio-temporal graph-based cnns for traffic prediction[J]. arXiv preprint arXiv:1812.02019, 2018. Link

• 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