不看后悔!基于图神经网络的交通预测论文合集

最近啊~学姐为了工作在研究时间序列方面的知识,我发现交通预测真的很有趣,然后就准备深入研究一下,第一步就是去找论文啦~都说读论文头秃,其实找论文也头秃,学姐的头发又少了一层。

所以为了让我可爱的粉丝们以后不用再花植发的钱,再买护肝片,再保温杯里热牛奶泡枸杞,学姐决定把我的劳动成果——找到的有关于“图神经网络的交通预测的论文”贡献给大家!需要的就自取叭!(但能不能麻烦各位给点个👍,qvq)

交通预测图神经网络论文合集

Journal(期刊)47篇

01

作者:

Xia T, Lin J, Li Y, et al.

论文名称:

3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction[J].

刊名及日期:

ACM Transactions on Knowledge Discovery from Data (TKDD), 2021, 15(6): 1-21.

论文链接:

https://dl.acm.org/doi/abs/10.1145/3451394

代码:

https://github.com/FIBLAB/3D-DGCN

02

作者:

Zhang H, Chen L, Cao J, et al.

论文名称:

A Combined Traffic Flow Forecasting Model Based on Graph Convolutional Network and Attention Mechanism[J].

刊名及日期:

International Journal of Modern Physics C, 2021.

论文链接:

https://www.worldscientific.com/doi/abs/10.1142/S0129183121501588

03

作者:

Zhang T, Ding W, Chen T, et al.

论文名称:

A Graph Convolutional Method for Traffic Flow Prediction in Highway Network[J].

刊名及日期:

Wireless Communications and Mobile Computing, 2021, 2021.

论文链接:https://www.hindawi.com/journals/wcmc/2021/1997212/

04

作者:

Chen P, Fu X, Wang X.

论文名称:

A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9381554

论文链接:https://www.hindawi.com/journals/wcmc/2021/1997212/

05

作者:

Zhang S, Guo Y, Zhao P, et al.

论文名称:

A Graph-Based Temporal Attention Framework for Multi-Sensor Traffic Flow Forecasting[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9406409/

代码:

https://github.com/skzhangPKU/GTA

06

作者:

Han Y, Peng T, Wang C, et al.

论文名称:

A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow[J].

刊名及日期:

ISPRS International Journal of Geo-Information, 2021, 10(4): 222.

论文链接:

https://www.mdpi.com/1059488

07

作者:

Chen L, Bei L, An Y, et al.

论文名称:

A Hyperparameters automatic optimization method of time graph convolution network model for traffic prediction[J].

刊名及日期:

Wireless Networks, 2021: 1-9.

论文链接:

https://link.springer.com/article/10.1007/s11276-021-02672-5

08

作者:

Feng S, Ke J, Yang H, et al.

论文名称:

A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9376697/

09

作者:

Zhu J, Tao C, Deng H, et al.

论文名称:

AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting[J].

刊名及日期:

IEEE Access.

论文链接:

https://ieeexplore.ieee.org/document/9363197

代码:

https://github.com/lehaifeng/T-GCN/tree/master/AST-GCN

10

作者:

Buroni G, Lebichot B, Bontempi G.

论文名称:

AST-MTL: An Attention-based Multi-Task Learning Strategy for Traffic Forecasting[J].

刊名及日期:

IEEE Access, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9439877/

代码:

https://github.com/giobbu/AST-MTL

11

作者:

Jiang H, Li L, Xian H, et al.

论文名称:

Crowd Flow Prediction for Social Internet-of-Things Systems Based on the Mobile Network Big Data[J].

刊名及日期:

IEEE Transactions on Computational Social Systems, 2021.

论文链接:https://ieeexplore.ieee.org/abstract/document/9378810/

12

作者:

Pan C, Zhu J, Kong Z, et al.

论文名称:

DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting[J].

刊名及日期:

Electronics, 2021, 10(9): 1014.

论文链接:

https://www.mdpi.com/1085378

13

作者:

Bai L, Yao L, Wang X, et al.

论文名称:

Deep spatial-temporal sequence modeling for multi-step passenger demand prediction[J].

刊名及日期:

Future Generation Computer Systems, 2021.

论文链接:https://www.sciencedirect.com/science/article/pii/S0167739X21000832

14

作者:

Zhang C, Zhang S, James J Q, et al.

论文名称:

FASTGNN: A Topological Information Protected Federated Learning Approach For Traffic Speed Forecasting[J].

刊名及日期:

IEEE Transactions on Industrial Informatics, 2021.

论文链接:https://ieeexplore.ieee.org/abstract/document/9340313

15

作者:

Yang X, Zhu Q, Li P, et al.

论文名称:

Fine-grained predicting urban crowd flows with adaptive spatio-temporal graph convolutional network[J].

刊名及日期:

Neurocomputing, 2021, 446: 95-105.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0925231221003477

16

作者:

Fang M, Tang L, Yang X, et al.

论文名称:

FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:https://ieeexplore.ieee.org/abstract/document/9329073

17

作者:

Wang X, Chai Y, Li H, et al.

论文名称:

Graph Convolutional Network-based Model for Incident-related Congestion Prediction: A Case Study of Shanghai Expressways[J].

刊名及日期:

ACM Transactions on Management Information Systems (TMIS), 2021, 12(3): 1-22.

论文链接:

https://dl.acm.org/doi/abs/10.1145/3451356

18

作者:

Wang Q, Xu C, Zhang W, et al.

论文名称:

GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs[J].

刊名及日期:

IEEE Signal Processing Letters, 2021.

论文链接:https://ieeexplore.ieee.org/abstract/document/9314202

19

作者:

Jin C, Ruan T, Wu D, et al.

论文名称:

HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction[J].

刊名及日期:

Journal of Ambient Intelligence and Humanized Computing, 2021.

论文链接:

https://link.springer.com/article/10.1007/s12652-020-02807-0

20

作者:

An J, Guo L, Liu W, et al.

论文名称:

IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction[J].

刊名及日期:

Neural Networks, 2021.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0893608021002318

21

作者:

Ke J, Feng S, Zhu Z, et al.

论文名称:

Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based Approach[J].

刊名及日期:

Transportation Research Part C: Emerging Technologies, 2021, 127: 103063.

论文链接:

https://www.sciencedirect.com/science/article/abs/pii/S0968090X21000905

22

作者:

Guo S, Lin Y, Wan H, et al.

论文名称:

Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting[J].

刊名及日期:

IEEE Transactions on Knowledge and Data Engineering, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9346058

23

作者:

Zou X, Zhang S, Zhang C, et al.

论文名称:

Long-term Origin-Destination Demand Prediction with Graph Deep Learning[J].

刊名及日期:

IEEE Transactions on Big Data, 2021.

论文链接:

https://ieeexplore.ieee.org/document/9369004

24

作者:

James J Q, Markos C, Zhang S.

论文名称:

Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

论文链接:

https://ieeexplore.ieee.org/abstract/document/9399848/

25

作者:

Fang Z, Pan L, Chen L, et al.

论文名称:

MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data[J].

刊名及日期:

Proc. VLDB Endow., 2021, 14(8): 1289-1297.

论文链接:

http://www.vldb.org/pvldb/vol14/p1289-gao.pdf

26

作者:

Zhao D, Ju C, Zhu G, et al.

论文名称:

MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9387634/

27

作者:

Wang J, Zhang Y, Wei Y, et al.

论文名称:

Metro Passenger Flow Prediction via Dynamic Hypergraph Convolution Networks[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.  (still empty on 2021/5/8)

论文链接:

https://ieeexplore.ieee.org/abstract/document/9410439/

代码:

https://github.com/JCwww/DSTHGCN

28

作者:

Sun B, Zhao D, Shi X, et al.

论文名称:

Modeling Global Spatial–Temporal Graph Attention Network for Traffic Prediction[J].

刊名及日期:

IEEE Access, 2021.

论文链接:

https://ieeexplore.ieee.org/document/9316302

29

作者:

Tang J, Liang J, Liu F, et al.

论文名称:

Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network[J].

刊名及日期:

Transportation Research Part C: Emerging Technologies, 2021, 124: 102951.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0968090X20308482

30

作者:

Li G, Knoop V L, van Lint H.

论文名称:

Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations[J].

刊名及日期:

Transportation Research Part C: Emerging Technologies, 2021, 128: 103185.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0968090X21002011

代码:

https://github.com/RomainLITUD/DGCN_traffic_forecasting

31

作者:

Fang S, Prinet V, Chang J, et al.

论文名称:

MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9385959/

32

作者:

Wang F, Xu J, Liu C, et al.

论文名称:

On prediction of traffic flows in smart cities: a multitask deep learning based Approach[J].

刊名及日期:

World Wide Web, 2021: 1-19.

论文链接:

https://link.springer.com/article/10.1007/s11280-021-00877-4

33

作者:

Liu M, Li L, Li Q, et al.

论文名称:

Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network[J].

刊名及日期:

ISPRS International Journal of Geo-Information, 2021, 10(7): 455.

论文链接:

https://www.mdpi.com/2220-9964/10/7/455

34

作者:

Ke J, Qin X, Yang H, et al.

论文名称:

Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network[J].

刊名及日期:

Transportation Research Part C: Emerging Technologies, 2021, 122: 102858.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0968090X20307580

代码:

https://github.com/kejintao/ST-ED-RMGC

35

作者:

Li M, Gao S, Lu F, et al.

论文名称:

Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks[J].

刊名及日期:

International Journal of Geographical Information Science, 2021: 1-28.

论文链接:

https://www.tandfonline.com/doi/abs/10.1080/13658816.2021.1912347

代码:https://doi.org/10.6084/m9.figshare.11829306.v1

36

作者:

Yang J M, Peng Z R, Lin L.

论文名称:

Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization[J].

刊名及日期:

Transportation Research Part C: Emerging Technologies, 2021, 129: 103228.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0968090X21002412

代码:

https://github.com/Vadermit/TransPAI

38

作者:

Jiang M, Chen W, Li X.

论文名称:

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].

刊名及日期:

Journal of Data, Information and Management, 1-20.

论文链接:

https://link.springer.com/article/10.1007/s42488-020-00037-9

39

作者:

Agafonov A A.

论文名称:

Short-Term Traffic Data Forecasting: A Deep Learning Approach[J].

刊名及日期:

Optical Memory and Neural Networks, 2021, 30(1): 1-10.

论文链接:

https://link.springer.com/article/10.3103/S1060992X21010021

代码:

https://github.com/ant-agafonov/stgcn-lstm

40

作者:

Tian C, Chan W K.

论文名称:

Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies[J].

刊名及日期:

IET Intelligent Transport Systems, 2021.

论文链接:

https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12044

代码:

https://github.com/CYBruce/STAWnet

41

作者:

Bui K H N, Cho J, Yi H.

论文名称:

Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues[J].

刊名及日期:

Applied Intelligence, 2021: 1-12.

论文链接:

https://link.springer.com/article/10.1007/s10489-021-02587-w

42

作者:

Li D, Lasenby J.

论文名称:

Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction[J].

刊名及日期:

IEEE Transactions on Intelligent Transportation Systems, 2021.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9442362/

43

作者:

Li X, Wang H, Sun P, et al.

论文名称:

Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model[J].

刊名及日期:

Sustainability 2021, 13, 1253.

论文链接:

https://www.mdpi.com/2071-1050/13/3/1253

44

作者:

Tang J, Zeng J.

论文名称:

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data[J].

刊名及日期:

Computer‐Aided Civil and Infrastructure Engineering, 2021.

论文链接:

https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12688

45

作者:

Zi W, Xiong W, Chen H, et al.

论文名称:

TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network[J].

刊名及日期:

Information Sciences, 2021, 561: 274-285.

论文链接:

https://www.sciencedirect.com/science/article/pii/S0020025521001031

46

作者:

Zhang J, Chen H, Fang Y.

论文名称:

TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data[J].

刊名及日期:

Journal of Electrical and Computer Engineering, 2021, 2021.

论文链接:

https://www.hindawi.com/journals/jece/2021/9956406/

47

作者:

Xu C, Zhang A, Xu C, et al.

论文名称:

Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features[J].

刊名及日期:

Applied Intelligence, 2021: 1-19.

论文链接:

https://link.springer.com/article/10.1007/s10489-021-02461-9

代码:

https://ieee-dataport.org/documents/pems

Conference(会议)25篇

01

作者:

Chen Z, Wu H, O'Connor N E, et al.

论文名称:

A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes[C].

会议名称及时间:

2021 IEEE 24rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2021.

论文链接:

https://arxiv.org/abs/2104.10644

02

作者:

Li B, Guo T, Wang Y, et al.

论文名称:

Adaptive Graph Co-Attention Networks for Traffic Forecasting[C]

会议名称及时间:

//PAKDD (1). 2021: 263-276.

论文链接:

https://link.springer.com/chapter/10.1007/978-3-030-75762-5_22

03

作者:

Lee H, Park C, Jin S, et al.

论文名称:

An Empirical Experiment on Deep Learning Models for Predicting Traffic Data[C].

会议名称及时间:

Accepted at 37th IEEE International Conference on Data Engineering (ICDE 2021), 2021.

论文链接:

https://arxiv.org/abs/2105.05504

04

作者:

Ye J, Sun L, Du B, et al.

论文名称:

Coupled Layer-wise Graph Convolution for Transportation Demand Prediction[C].

会议名称及时间:

Proceedings of the AAAI Conference on Artificial Intelligence. 2021.

论文链接:

https://arxiv.org/abs/2012.08080

代码:

https://github.com/Essaim/CGCDemandPrediction

05

作者:

Meng C, Rambhatla S, Liu Y.

论文名称:

Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling[C].

会议名称及时间:

In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '21). Association for Computing Machinery. 2021.

论文链接:

https://arxiv.org/abs/2106.05223

06

作者:

Shang C, Chen J, Bi J.

论文名称:

Discrete Graph Structure Learning for Forecasting Multiple Time Series[C].

会议名称及时间:

International Conference on Learning Representations (ICLR), 2021.

论文链接:

https://openreview.net/forum?id=WEHSlH5mOk

代码:

https://github.com/chaoshangcs/GTS

07

作者:

Oreshkin B N, Amini A, Coyle L, et al.

论文名称:

FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting[C].

会议名称及时间:

Proceedings of the AAAI Conference on Artificial Intelligence. 2021.

论文链接:

https://arxiv.org/abs/2007.15531v1

代码:

https://github.com/boreshkinai/fc-gaga

08

作者:

Dees B S, Xu Y L, Constantinides A G, et al.

论文名称:

Graph Theory for Metro Traffic Modelling[C].

会议名称及时间:

International Joint Conference on Neural Networks (IJCNN), 2021.

论文链接:

https://arxiv.org/abs/2105.04991

09

作者:

Guo K, Hu Y, Sun Y, et al.

论文名称:

Hierarchical Graph Convolution Networks for Traffic Forecasting[C].

会议名称及时间:

Proceedings of the AAAI Conference on Artificial Intelligence. 2021.

论文链接:

https://github.com/guokan987/HGCN/blob/main/paper/3399.GuoK.pdf

代码:

https://github.com/guokan987/HGCN

10

作者:

Wang S, Zhang M, Miao H, et al.

论文名称:

MT-STNets: Multi-Task Spatial-Temporal Networks for Multi-Scale Traffic Prediction[C]

会议名称及时间:

//Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2021: 504-512.

论文链接:

https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.57

11

作者:

Jing B, Tong H, Zhu Y.

论文名称:

Network of Tensor Time Series[C].

会议名称及时间:

Accepted by WWW 2021.

论文链接:

https://arxiv.org/abs/2102.07736

12

作者:

Lin H, Fan Y, Zhang J, et al.

论文名称:

REST: Reciprocal Framework for Spatiotemporal-coupled Predictions[C]

会议名称及时间:

//Proceedings of the Web Conference 2021. 2021: 3136-3145.

论文链接:

https://dl.acm.org/doi/abs/10.1145/3442381.3449928

13

作者:

Pal S, Ma L, Zhang Y, et al.

论文名称:

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting[C].

会议名称及时间:

Accepted at the International Conference on Machine Learning (ICML) 2021.

论文链接:

https://arxiv.org/abs/2106.06064

代码:

https://github.com/networkslab/rnn_flow

14

作者:

Yang G, Wen J, Yu D, et al.

论文名称:

Spatial-Temporal Dilated and Graph Convolutional Network for traffic prediction[C]

会议名称及时间:

//2020 Chinese Automation Congress (CAC). IEEE, 2020: 802-806.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9327103/

15

作者:

Mengzhang L, Zhanxing Z.

论文名称:

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting[C].

会议名称及时间:

Proceedings of the AAAI Conference on Artificial Intelligence. 2021.

论文链接:

https://arxiv.org/abs/2012.09641

代码:

https://github.com/MengzhangLI/STFGNN

16

作者:

Fang Z, Long Q, Song G, et al.

论文名称:

Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting[C].

会议名称及时间:

In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '21). Association for Computing Machinery. 2021.

论文链接:

https://arxiv.org/abs/2106.12931

代码:

https://github.com/square-coder/STGODE

17

作者:

Hong G, Wang Z, Han T, et al.

论文名称:

Spatiotemporal Multi-Graph Convolutional Network for Taxi Demand Prediction[C]

会议名称及时间:

//2021 11th International Conference on Information Science and Technology (ICIST). IEEE, 2021: 242-250.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9440573/

18

作者:

Roy A, Roy K K, Ali A A, et al.

论文名称:

SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network[C].

会议名称及时间:

Accepted for publication in 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021).

论文链接:

https://arxiv.org/abs/2104.00055

19

作者:

Fu H, Wang Z, Yu Y, et al.

论文名称:

Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-Hailing Demand Forecasting[C]

会议名称及时间:

//PAKDD (1). 2021: 754-765.

论文链接:

https://link.springer.com/chapter/10.1007/978-3-030-75762-5_59

20

作者:

Zhang X, Huang C, Xu Y, Xia L, et al.

论文名称:

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network[C].

会议名称及时间:

Proceedings of the AAAI Conference on Artificial Intelligence. 2021.

论文链接:

http://urban-computing.com/pdf/AAAI2021TrafficFlow.pdf

代码:

https://github.com/jillbetty001/ST-GDN

21

作者:

Li M, Tong P, Li M, et al.

论文名称:

Traffic Flow Prediction with Vehicle Trajectories[C].

会议名称及时间:

Proceedings of the AAAI Conference on Artificial Intelligence. 2021.

论文链接:

https://wands.sg/publications/full_list/papers/AAAI_21_1.pdf

代码:

https://github.com/mingqian000/TrGNN

22

作者:

Yang Q, Zhong T, Zhou F.

论文名称:

Traffic Speed Forecasting Via Spatio-Temporal Attentive Graph Isomorphism Network[C]

会议名称及时间:

//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 7943-7947.

论文链接:

https://ieeexplore.ieee.org/abstract/document/9414596/

23

作者:

Chen X, Wang J, Xie K.

论文名称:

TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning[C]

会议名称及时间:

//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI. 2021.

论文链接:

https://arxiv.org/abs/2106.06273

代码:

https://github.com/AprLie/TrafficStream

24

作者:

Roy A, Roy K K, Ali A A, et al.

论文名称:

Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network[C].

会议名称及时间:

2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.

论文链接:

https://arxiv.org/abs/2104.12518

代码:

https://github.com/AmitRoy7781/USTGCN

25

作者:

Chen Y, Segovia-Dominguez I, Gel Y R.

论文名称:

Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting[C].

会议名称及时间:

Accepted at the International Conference on Machine Learning (ICML) 2021.

论文链接:

https://arxiv.org/abs/2105.04100

代码:

https://github.com/Z-GCNETs/Z-GCNETs.git

Preprint(文章)11篇

01

作者:

Fu J, Zhou W, Chen Z.

文章名称:

Bayesian Graph Convolutional Network for Traffic Prediction[J].

编号及日期:

arXiv preprint arXiv:2104.00488, 2021.

论文链接:

https://arxiv.org/abs/2104.00488

02

作者:

Lin H, Gao Z, Wu L, et al.

文章名称:

Conditional Local Filters with Explainers for Spatio-Temporal Forecasting[J].

编号及日期:

arXiv preprint arXiv:2101.01000, 2021.

论文链接:

https://arxiv.org/abs/2101.01000v1

03

作者:

Li F, Feng J, Yan H, et al.

文章名称:

Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution[J].

编号及日期:

arXiv preprint arXiv:2104.14917, 2021.

论文链接:

https://arxiv.org/abs/2104.14917

代码:

https://github.com/tsinghua-fib-lab/Traffic-Benchmark

04

作者:

Chen J, Li K, Li K, et al.

文章名称:

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network[J].

编号及日期:

arXiv preprint arXiv:2101.07425, 2021. Link

论文链接:

https://arxiv.org/abs/2101.07425

05

作者:

Lu Y, Ding H, Ji S, et al.

文章名称:

Dual attentive graph neural network for metro passenger flow prediction[J].

编号及日期:

Researchgate preprint. Link

论文链接:

https://www.researchgate.net/publication/350372196_Dual_attentive_graph_neural_network_for_metro_passenger_flow_prediction

06

作者:

Li Y, Wang D, Moura J M F.

文章名称:

GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention[J].

编号及日期:

arXiv preprint arXiv:2104.05914, 2021.

论文链接:

https://arxiv.org/abs/2104.05914

07

作者:

Ye J, Zheng F, Zhao J, et al.

文章名称:

Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting[J].

编号及日期:

arXiv preprint arXiv:2107.01528, 2021.

论文链接:

https://arxiv.org/abs/2107.01528

08

作者:

Li M, Chen S, Shen Y, et al.

文章名称:

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network[J].

编号及日期:

arXiv preprint arXiv:2107.00894, 2021.

论文链接:

https://arxiv.org/abs/2107.00894

09

作者:

Wang Y, Yin H, Chen T, et al.

文章名称:

Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph[J].

编号及日期:

arXiv preprint arXiv:2101.00752, 2021.

论文链接:

https://arxiv.org/abs/2101.00752

10

作者:

Jin G, Yan H, Li F, et al.

文章名称:

Spatial-Temporal Dual Graph Neural Networks for Travel Time Estimation[J].

编号及日期:

arXiv preprint arXiv:2105.13591, 2021.

论文链接:

https://arxiv.org/abs/2105.13591

11

作者:

Xu X, Zhang T, Xu C, et al.

文章名称:

Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction[J].

编号及日期:

arXiv preprint arXiv:2103.06126, 2021.

论文链接:

https://arxiv.org/abs/2103.06126

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传送门:

https://github.com/Jhy1993/Awesome-GNN-Recommendation

参考文档:

https://github.com/Jhy1993/Awesome-GNN-Recommendation

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