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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.

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

  • 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

  • Azzedine Boukerche, Jiahao Wang, A Performance Modeling and Analysis of a Novel Vehicular Traffic Flow Prediction System Using a Hybrid Machine Learning-Based Model, Ad Hoc Networks, 2020. 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

  • Zhang J, Chen F, Cui Z, et al. Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link Code

  • 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

  • Xiong X, Ozbay K, Jin L, et al. Dynamic Origin–Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter[J]. Transportation Research Record, 2020: 0361198120919399. Link Code

  • 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

  • Zhou Q, Gu J J, Ling C, et al. Exploiting Multiple Correlations Among Urban Regions for Crowd Flow Prediction[J]. Journal of Computer Science and Technology, 2020, 35: 338-352. 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]. Transportation Research Part C: Emerging Technologies, 2020, 119. Link

  • 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

  • Xu D, Wei C, Peng P, et al. GE-GAN: A novel deep learning framework for road traffic state estimation[J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102635. 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

  • Zhang T, Guo G. Graph Attention LSTM: A Spatio-Temperal Approach for Traffic Flow Forecasting[J]. IEEE Intelligent Transportation Systems Magazine, 2020. Link

  • Cui Z, Lin L, Pu Z, et al. Graph Markov Network for Traffic Forecasting with Missing Data[J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102671. Link

  • Mallick T, Balaprakash P, Rask E, et al. Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting[J]. Transportation Research Record, 2020: 0361198120930010. Link

  • Davis N, Raina G, Jagannathan K. Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link Code

  • Chen Z, Zhao B, Wang Y, et al. Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network[J]. Sensors, 2020, 20(13): 3776. 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

  • Zhang J, Chen F, Guo Y. Multi-Graph Convolutional Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit[J]. IET Intelligent Transport Systems, 2020. Link Code

  • 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

  • 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

  • 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 recurrent neural network and traffic simulation[J]. IET Intelligent Transport Systems, 2020. Link

  • Guo W, Yuan W. Short-term traffic speed forecasting based on graph attention temporal convolutional networks[J]. Neurocomputing, 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

  • 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

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

  • 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

  • Zhao J, Qu H, Zhao J, et al. Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction[J]. Transactions on Emerging Telecommunications Technologies, e4056. Link

  • Kong X, Xing W, Wei X, et al. STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting[J]. IEEE Access, 2020. Link [Code (still empty on Aug 3, 2020)] (https://github.com/xyk0058/STGAT)

  • Lv M, Hong Z, Chen L, et al. Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 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

  • Cai L, Janowicz K, Mai G, et al. Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting[J]. Transactions in GIS. Link

  • 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

  • Zhou F, Yang Q, Zhong T, et al. Variational Graph Neural Networks for Road Traffic Prediction in Intelligent Transportation Systems[J]. IEEE Transactions on Industrial Informatics, 2020. Link

Conference

  • 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

  • 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

  • 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

  • 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

  • Li Y, Moura J M F. Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data[C]//Proceedings of the Twenty-fourth European Conference on Artificial Intelligence. 2020. 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

  • 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

  • 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

  • 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

  • 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

  • Yang C, Zhou Z, Wen H, et al. MSTNN: A Graph Learning Based Method for the Origin-Destination Traffic Prediction[C]//ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-6. Link

  • 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

  • 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

  • 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

  • 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

  • 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

  • Park C, Lee C, Bahng H, et al. ST-GRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM). 2020. Link

  • 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 2020/9/18)

  • 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

  • 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

  • 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

Preprint

  • 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

  • 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

  • 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

  • Oreshkin B N, Amini A, Coyle L, et al. FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting[J]. arXiv preprint arXiv:2007.15531, 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

  • 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

  • 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

  • Zhang Y, Lu M, Li H. Urban Traffic Flow Forecast Based on FastGCRNN[J]. arXiv preprint arXiv:2009.08087, 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

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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 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

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