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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]. Expert Systems with Applications, 2022. Link
Jiang W, Luo J. Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools[J]. Applied System Innovation. 2022; 5(1):23. Link
Jiang W. Bike sharing usage prediction with deep learning: a survey[J]. Neural Computing and Applications, 2022, 34(18): 15369-15385. Link
Jiang W, Luo J, He M, Gu W. Graph Neural Network for Traffic Forecasting: The Research Progress[J]. ISPRS International Journal of Geo-Information, 2023. Link

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

Advertisement: We would like to cordially invite you to submit a paper to our special issue on "Graph Neural Network for Traffic Forecasting" for Information Fusion (SCI-indexed, Impact Factor: 17.564).

Special issue website: https://www.sciencedirect.com/journal/information-fusion/about/call-for-papers#graph-neural-network-for-traffic-forecasting
Deadline for manuscript submissions: 1 December 2023.

Advertisement: We would like to cordially invite you to submit a paper to our Topical Collection on "Deep Neural Networks for Traffic Forecasting" for Neural Computing and Applications (SCI-indexed, Impact Factor: 6.0).

Topical Collection website: https://www.springer.com/journal/521/updates/26215426
Deadline for manuscript submissions: 1 April 2024.

Advertisement: If you are interested in maintaining this repository, feel free to drop me an email.

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
Awesome-Mobility-Machine-Learning-Contents Link
Traffic Prediction Link
Paper & Code & Dataset Collection of Spatial-Temporal Data Mining. Link

Relevant Data Repositories

Strategic Transport Planning Dataset Link

Description: A graph based strategic transport planning dataset, aimed at creating the next generation of deep graph neural networks for transfer learning. Based on simulation results of the Four Step Model in PTV Visum. Relevant Thesis: Development of a Deep Learning Surrogate for the Four-Step Transportation Model

Zhang Y, Gong Q, Chen Y, et al. A Human Mobility Dataset Collected via LBSLab[J]. Data in Brief, 2023: 108898. Link Data
Jiang R, Cai Z, Wang Z, et al. Yahoo! Bousai Crowd Data: A Large-Scale Crowd Density and Flow Dataset in Tokyo and Osaka[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 6676-6677. Link Data

2024

Journal

Ju W, Zhao Y, et al. COOL: A conjoint perspective on spatio-temporal graph neural network for traffic forecasting[J]. Information Fusion, 2024. Link
Fang S, Ji W, Xiang S, et al. PreSTNet: Pre-trained Spatio-Temporal Network for traffic forecasting[J]. Information Fusion, 2024, 106: 102241. Link Code

Preprint

Li H, Zhao Y, et al. A Survey on Graph Neural Networks in Intelligent Transportation Systems[J]. arXiv preprint arXiv:2401.00713, 2024. Link

2023

Journal

Qi X, Yao J, Wang P, et al. Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach[J]. IET Intelligent Transport Systems, 2023. Link
Tian R, Wang C, Hu J, et al. MFSTGN: a multi-scale spatial-temporal fusion graph network for traffic prediction[J]. Applied Intelligence, 2023: 1-20. Link
Zhao W, Zhang S, Zhou B, et al. Multi-spatio-temporal Fusion Graph Recurrent Network for Traffic Forecasting[J]. Engineering Applications of Artificial Intelligence, 2023, 124: 106615. Link
Zhou J, Qin X, Ding Y, et al. Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting[J]. Mathematics, 2023, 11(13): 2867. Link
Wang C, Wang L, Wei S, et al. STN-GCN: Spatial and Temporal Normalization Graph Convolutional Neural Networks for Traffic Flow Forecasting[J]. Electronics, 2023, 12(14): 3158. Link
Cheng X, He Y, Zhang P, et al. Traffic flow prediction based on information aggregation and comprehensive temporal-spatial synchronous graph neural network[J]. IEEE Access, 2023. Link
Zhao Z, Shen G, Zhou J, et al. Spatial-temporal hypergraph convolutional network for traffic forecasting[J]. PeerJ Computer Science, 2023, 9: e1450. Link Code
Liang G, Kintak U, Ning X, et al. Semantics-aware dynamic graph convolutional network for traffic flow forecasting[J]. IEEE Transactions on Vehicular Technology, 2023. Link Code
Wen Y, Li Z, Wang X, et al. Traffic demand prediction based on spatial-temporal guided multi graph Sandwich-Transformer[J]. Information Sciences, 2023: 119269. Link Code
Hu S, Ye Y, Hu Q, et al. A Federated Learning-Based Framework for Ride-sourcing Traffic Demand Prediction[J]. IEEE Transactions on Vehicular Technology, 2023. Link
Ouyang X, Yang Y, Zhou W, et al. CityTrans: Domain-Adversarial Training with Knowledge Transfer for Spatio-Temporal Prediction across Cities[J]. IEEE Transactions on Knowledge and Data Engineering, 2023. Link
Hu C, Liu X, Wu S, et al. Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure[J]. Applied Sciences, 2023, 13(12): 7271. Link
Ma C, Sun K, Chang L, et al. Enhanced Information Graph Recursive Network for Traffic Forecasting[J]. Electronics, 2023, 12(11): 2519. Link
García-Sigüenza J, Llorens-Largo F, Tortosa L, et al. Explainability techniques applied to road traffic forecasting using Graph Neural Network models[J]. Information Sciences, 2023: 119320. Link
Liu T, Jiang A, Zhou J, et al. GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Yu W, Huang X, Qiu Y, et al. GSTC-Unet: A U-shaped multi-scaled spatiotemporal graph convolutional network with channel self-attention mechanism for traffic flow forecasting[J]. Expert Systems with Applications, 2023: 120724. Link
Li Z, Han Y, Xu Z, et al. PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting[J]. ISPRS International Journal of Geo-Information, 2023, 12(6): 241. Link
Ning T, Wang J, Duan X. Research on expressway traffic flow prediction model based on MSTA-GCN[J]. Journal of Ambient Intelligence and Humanized Computing, 2022: 1-12. Link
Zhang Q, Li C, Su F, et al. Spatio-Temporal Residual Graph Attention Network for Traffic Flow Forecasting[J]. IEEE Internet of Things Journal, 2023. Link
Chang Z, Liu C, Jia J. STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction[J]. Applied Sciences, 2023, 13(11): 6796. Link
Yin L, Liu P, Wu Y, et al. ST-VGBiGRU: A Hybrid Model for Traffic Flow Prediction With Spatio-temporal Multimodality[J]. IEEE Access, 2023. Link
Zheng G, Chai W K, Zhang J, et al. VDGCNeT: A novel network-wide Virtual Dynamic Graph Convolution Neural network and Transformer-based traffic prediction model[J]. Knowledge-Based Systems, 2023: 110676. Link
Weng W, Fan J, Wu H, et al. A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting[J]. Pattern Recognition, 2023: 109670. Link Code
Corrias R, Gjoreski M, Langheinrich M. Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling[J]. Sensors, 2023, 23(10): 4803. Link Code
Lablack M, Shen Y. Spatio-temporal graph mixformer for traffic forecasting[J]. Expert Systems with Applications, 2023, 228: 120281. Link Code
Zhao J, Zhang R, Sun Q, et al. Adaptive graph convolutional network-based short-term passenger flow prediction for metro[J]. Journal of Intelligent Transportation Systems, 2023: 1-10. Link
Chen Y, Qin Y, Li K, et al. Adaptive Spatial-Temporal Graph Convolution Networks for Collaborative Local-Global Learning in Traffic Prediction[J]. IEEE Transactions on Vehicular Technology, 2023. Link
Wang B, Gao F, Tong L, et al. Channel attention-based spatial-temporal graph neural networks for traffic prediction[J]. Data Technologies and Applications, 2023. Link
Cao Y, Liu L, Dong Y. Convolutional Long Short-Term Memory Two-Dimensional Bidirectional Graph Convolutional Network for Taxi Demand Prediction[J]. Sustainability, 2023, 15(10): 7903. Link
Zhao T, Huang Z, Tu W, et al. Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus[J]. International Journal of Geographical Information Science, 2023: 1-27. Link
Karim S, Mehmud M, Alamgir Z, et al. Dynamic Spatial Correlation in Graph WaveNet for Road Traffic Prediction[J]. Transportation Research Record, 2023: 03611981221151024. Link
Yue W, Zhou D, Wang S, et al. Engineering Traffic Prediction With Online Data Imputation: A Graph-Theoretic Perspective[J]. IEEE Systems Journal, 2023. Link
Feng X, Chen Y, Li H, et al. Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction[J]. Sustainability, 2023, 15(9): 7696. Link
Ni Q, Peng W, Zhu Y, et al. Graph dropout self-learning hierarchical graph convolution network for traffic prediction[J]. Engineering Applications of Artificial Intelligence, 2023, 123: 106460. Link
Hu Y, Peng T, Guo K, et al. Graph transformer based dynamic multiple graph convolution networks for traffic flow forecasting[J]. IET Intelligent Transport Systems, 2023. Link
Zheng W, Yang H F, Cai J, et al. Integrating the traffic science with representation leaning for city-wide network congestion prediction[J]. Information Fusion, 2023: 101837. Link
Wang S, Zhang Y, Hu Y, et al. Knowledge fusion enhanced graph neural network for traffic flow prediction[J]. Physica A: Statistical Mechanics and its Applications, 2023: 128842. Link
Luo C, Cai R, Guo H, et al. MG-ASTN: Multi-Graph Framework with Attentive Spatial-Temporal Networks for Crowd Mobility Prediction[J]. IEEE Internet of Things Journal, 2023. Link
Liu L, Tian Y, Chakraborty C, et al. Multilevel Federated Learning based Intelligent Traffic Flow Forecasting for Transportation Network Management[J]. IEEE Transactions on Network and Service Management, 2023. Link
Li J, Wu P, Guo H, et al. Multivariate Transfer Passenger Flow Forecasting with Data Imputation by Joint Deep Learning and Matrix Factorization[J]. Applied Sciences, 2023, 13(9): 5625. Link
Han X, Zhu G, Zhao L, et al. Ollivier–Ricci Curvature Based Spatio-Temporal Graph Neural Networks for Traffic Flow Forecasting[J]. Symmetry, 2023, 15(5): 995. Link
Liu X, Zeng J, Zhu R, et al. PGSLM: Edge-enabled probabilistic graph structure learning model for traffic forecasting in Internet of vehicles[J]. China Communications, 2023, 20(4): 270-286. Link
Xu M, Qiu T Z, Fang J, et al. Signal-control Refined Dynamic Traffic Graph Model for Movement-based Arterial Network Traffic Volume Prediction[J]. Expert Systems with Applications, 2023: 120393. Link
Su Z, Liu T, Hao X, et al. Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters[J]. The Journal of Supercomputing, 2023: 1-20. Link
Yin X, Zhang W, Zhang S. Spatiotemporal dynamic graph convolutional network for traffic speed forecasting[J]. Information Sciences, 2023: 119056. Link
Qu Y, Rong J, Li Z, et al. ST-A-PGCL: Spatiotemporal adaptive periodical graph contrastive learning for traffic prediction under real scenarios[J]. Knowledge-Based Systems, 2023, 272: 110591. Link
Yin X, Zhang W, Jing X. Static-dynamic collaborative graph convolutional network with meta-learning for node-level traffic flow prediction[J]. Expert Systems with Applications, 2023: 120333. Link
He S, Luo Q, Du R, et al. STGC-GNNs: A GNN-based traffic prediction framework with a spatial–temporal Granger causality graph[J]. Physica A: Statistical Mechanics and its Applications, 2023: 128913. Link
Trirat P, Yoon S, Lee J G. MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link Code
Bao Y, Huang J, Shen Q, et al. Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106044. Link Code
Yu Q, Zhang Y, Guo J, et al. A multiple spatio‐temporal features fusion approach for short‐term passenger flow forecasting in urban rail transit[J]. IET Intelligent Transport Systems, 2023. Link
Liu T, Zhang J. An adaptive traffic flow prediction model based on spatiotemporal graph neural network[J]. The Journal of Supercomputing, 2023: 1-25. Link
Chen L, Ren Q, Zeng J, et al. CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction[J]. Plos one, 2023, 18(4): e0283898. Link
Li H, Jin D, Li X, et al. DMGF-Net: An Efficient Dynamic Multi-Graph Fusion Network for Traffic Prediction[J]. ACM Transactions on Knowledge Discovery from Data, 2023. Link
Zhang H, Kan S, Zhang X J, et al. Dynamic Spatial–Temporal Convolutional Networks for Traffic Flow Forecasting[J]. Transportation Research Record, 2023: 03611981231159407. Link
Brimos P, Karamanou A, Kalampokis E, et al. Graph Neural Networks and Open-Government Data to Forecast Traffic Flow[J]. Information, 2023, 14(4): 228. Link
Wang B, Zhang Y, Shi J, et al. Knowledge Expansion and Consolidation for Continual Traffic Prediction With Expanding Graphs[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Oluwasanmi A, Aftab M U, Qin Z, et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction[J]. Sensors, 2023, 23(8): 3836. Link
Liu Y, Wang C, Xu S, et al. Multi-weighted graph 3D convolution network for traffic prediction[J]. Neural Computing and Applications, 2023: 1-17. Link
Chen J, Wang W, Yu K, et al. Node Connection Strength Matrix-Based Graph Convolution Network for Traffic Flow Prediction[J]. IEEE Transactions on Vehicular Technology, 2023. Link
Zhai X, Shen Y. Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network[J]. Applied Sciences, 2023, 13(8): 4910. Link
Fafoutellis P, Vlahogianni E I. Traffic Demand Prediction Using a Social Multiplex Networks Representation on a Multimodal and Multisource Dataset[J]. International Journal of Transportation Science and Technology, 2023. Link
Ren Q, Li Y, Liu Y. Transformer-enhanced periodic temporal convolution network for long short-term traffic flow forecasting[J]. Expert Systems with Applications, 2023: 120203. Link
Trirat P, Yoon S, Lee J G. : Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link Code
Bao Y, Huang J, Shen Q, et al. Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106044. Link Code
Zhang X, Wang C, Chen J, et al. A deep neural network model with GCN and 3D convolutional network for short‐term metro passenger flow forecasting[J]. IET Intelligent Transport Systems, 2023. Link
Li B, Yang Q, Chen J, et al. A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction[J]. Journal of Advanced Transportation, 2023, 2023. Link
Liu L, Wu M, Chen R C, et al. A Hybrid Deep Learning Model for Multi-Station Classification and Passenger Flow Prediction[J]. Applied Sciences, 2023, 13(5): 2899. Link
Djenouri Y, Belhadi A, Djenouri D, et al. A Secure Intelligent System for Internet of Vehicles: Case Study on Traffic Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Zhang L, Ma J. A Spatiotemporal Graph Wavelet Neural Network for Traffic Flow Prediction[J]. Journal of Information and Intelligence, 2023. Link
Xu Y, Lu Y, Ji C, et al. Adaptive Graph Fusion Convolutional Recurrent Network for Traffic Forecasting[J]. IEEE Internet of Things Journal, 2023. Link
Liao Z, Huang H, Zhao Y, et al. Analysis and Forecast of Traffic Flow between Urban Functional Areas Based on Ride-Hailing Trajectories[J]. ISPRS International Journal of Geo-Information, 2023, 12(4): 144. Link
Liu L, Cao Y, Dong Y. Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting[J]. Sustainability, 2023, 15(6): 4697. Link
Ke S, Pan Z, He T, et al. AutoSTG+: An Automatic Framework to Discover The Optimal Network for Spatio-temporal Graph Prediction[J]. Artificial Intelligence, 2023: 103899. Link
Wang Y, Ren Q, Lv X, et al. CPNet: Conditionally parameterized graph convolutional network for traffic forecasting[J]. Physica A: Statistical Mechanics and its Applications, 2023: 128667. Link
Li H, Jin D, Li X, et al. DMGF-Net: An Efficient Dynamic Multi-Graph Fusion Network for Traffic Prediction[J]. ACM Transactions on Knowledge Discovery from Data, 2023. Link
Gu J, Jia Z, Cai T, et al. Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction[J]. Sensors, 2023, 23(6): 2897. Link
Wang D, Zhu J, Yin Y, et al. Dynamic travel time prediction with spatiotemporal features: using a GNN-based deep learning method[J]. Annals of Operations Research, 2023: 1-21. Link
Qi T, Chen L, Li G, et al. FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network[J]. Applied Soft Computing, 2023: 110175. Link
Liao L, Hu Z, Hsu C Y, et al. Fourier Graph Convolution Network for Time Series Prediction[J]. Mathematics, 2023, 11(7): 1649. Link
Rahmani S, Baghbani A, Bouguila N, et al. Graph Neural Networks for Intelligent Transportation Systems: A Survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Xiong H, Shen G, Lan X, et al. HIT-GCN: Spatial-Temporal Graph Convolutional Network Embedded with Heterogeneous Information of Road Network for Traffic Forecasting[J]. Electronics, 2023, 12(6): 1306. Link
Peng D, Zhang Y. MA-GCN: A Memory Augmented Graph Convolutional Network for traffic prediction[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106046. Link
Liao L, Li B, Zou F, et al. MFGCN: A Multimodal Fusion Graph Convolutional Network for Online Car-hailing Demand Prediction[J]. IEEE Intelligent Systems, 2023. Link
Zeng J, Tang J. Modeling Dynamic Traffic Flow as Visibility Graphs: A Network-Scale Prediction Framework for Lane-Level Traffic Flow Based on LPR Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Luo G, Zhang H, Yuan Q, et al. One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial–Temporal Dependency[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023. Link
Shuai C, Zhang X, Wang Y, et al. Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network[J]. IEEE Intelligent Transportation Systems Magazine, 2023. Link
Li J, Lin F, Han G, et al. PAG-TSN: Ridership Demand Forecasting Model for Shared Travel Services of Smart Transportation[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Bao Y, Liu J, Shen Q, et al. PKET-GCN: Prior Knowledge Enhanced Time-Varying Graph Convolution Network for Traffic Flow Prediction[J]. Information Sciences, 2023. Link
Liu M, Liu G, Sun L. Spatial-Temporal Dependence and Similarity Aware Traffic Flow Forecasting[J]. Information Sciences, 2023. Link
Wang Y, Ren Q, Li J. Spatial–temporal multi-feature fusion network for long short-term traffic prediction[J]. Expert Systems with Applications, 2023: 119959. Link
Cheng X, He Y, Zhang P, et al. Traffic flow prediction based on information aggregation and comprehensive temporal-spatial synchronous graph neural network[J]. IEEE Access, 2023. Link
Chen Y, Li K, Yeo C K, et al. Traffic forecasting with graph spatial-temporal position recurrent network[J]. Neural Networks, 2023. Link
Yao Z, Xia S, Li Y, et al. Transfer Learning With Spatial–Temporal Graph Convolutional Network for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Xu X, Hu X, Zhao Y, et al. Urban short-term traffic speed prediction with complicated information fusion on accidents[J]. Expert Systems with Applications, 2023: 119887. Link
Ge L, Jia Y, Li Q, et al. Dynamic multi-graph convolution recurrent neural network for traffic speed prediction[J]. Journal of Intelligent & Fuzzy Systems (Preprint): 1-14, 2023. Link
Luo R, Song Y, Huang L, et al. AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting[J]. Sensors, 2023, 23(4): 1975. Link Data
Li F, Feng J, Yan H, et al. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. Link Code
Tao S, Zhang H, Yang F, et al. Multiple Information Spatial-Temporal Attention Based Graph Convolution Network for traffic prediction[J]. Applied Soft Computing, 2023: 110052. Link Code
Liu S, Feng X, Ren Y, et al. DCENet: A dynamic correlation evolve network for short-term traffic prediction[J]. Physica A: Statistical Mechanics and its Applications, 2023: 128525. Link
Chang L, Ma C, Sun K, et al. Enhanced road information representation in graph recurrent network for traffic speed prediction[J]. IET Intelligent Transport Systems, 2023. Link
Feng F, Zou Z, Liu C, et al. Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model[J]. Sustainability, 2023, 15(4): 3296. Link
Liang G, Kintak U, Ning X, et al. Semantics-aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting[J]. IEEE Transactions on Vehicular Technology, 2023. Link
Yang Y, Shao X, Zhu Y, et al. Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather[J]. Journal of Advanced Transportation, 2023, 2023. Link
Zhang Q, Li C, Su F, et al. Spatio-Temporal Residual Graph Attention Network for Traffic Flow Forecasting[J]. IEEE Internet of Things Journal, 2023. Link
Zhang C, Zhang S, Zou X, et al. Towards Large-Scale Graph-Based Traffic Forecasting: A Data-Driven Network Partitioning Approach[J]. IEEE Internet of Things Journal, 2023. Link
Han Y, Zhao S, Deng H, et al. Principal graph embedding convolutional recurrent network for traffic flow prediction[J]. Applied Intelligence, 2023: 1-15. Link Code
He R, Xiao Y, Lu X, et al. ST-3DGMR: Spatio-temporal 3D grouped multiscale ResNet network for region-based urban traffic flow prediction[J]. Information Sciences, 2023, 624: 68-93. Link Code
Zhang J, Liu Y, Gui Y, et al. An Improved Model Combining Outlook Attention and Graph Embedding for Traffic Forecasting[J]. Symmetry, 2023, 15(2): 312. Link
Li Z L, Yu J, Zhang G W, et al. Dynamic spatio-temporal graph network with adaptive propagation mechanism for multivariate time series forecasting[J]. Expert Systems with Applications, 2023, 216: 119374. Link
Huo G, Zhang Y, Wang B, et al. Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Li P, Wang S, Zhao H, et al. IG-Net: An Interaction Graph Network Model for Metro Passenger Flow Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2023. Link
Li X, Wu J, He D, et al. Learning Spatial-Temporal Dynamics for Short-Term Passenger Flow Prediction in Urban Rail Transit[J]. Transportation Research Record, 2023: 03611981221143109. Link
Zeng H, Jiang C, Lan Y, et al. Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting[J]. Electronics, 2023, 12(1): 238. Link
Vidya G S, Hari V S. LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic[J]. Journal of Signal Processing Systems, 2023: 1-16. Link
Huang X, Wang J, Lan Y, et al. MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction[J]. Sensors, 2023, 23(2): 841. Link
Xu M, Di Y, Yang H, et al. Multi-task supply-demand prediction and reliability analysis for docked bike-sharing systems via transformer-encoder-based neural processes[J]. Transportation Research Part C: Emerging Technologies, 2023, 147: 104015. Link
Wu F, Zheng C, Zhang C, et al. Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting[J]. Applied Sciences, 2023, 13(2): 711. Link
Lee E, Choi H, Kim D G. PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport[J]. Journal of Advanced Transportation, 2023, 2023. Link
Huang R, Chen Z, Zhai G, et al. Spatial‐temporal correlation graph convolutional networks for traffic forecasting[J]. IET Intelligent Transport Systems, 2023. Link
Wei Z, Zhao H, Li Z, et al. STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(1): 226-238. Link
Wang Z, Ding D, Liang X. TYRE: A dynamic graph model for traffic prediction[J]. Expert Systems with Applications, 2023, 215: 119311. Link Code
Tygesen M N, Pereira F C, Rodrigues F. Unboxing the graph: Towards interpretable graph neural networks for transport prediction through neural relational inference[J]. Transportation Research Part C: Emerging Technologies, 2023, 146: 103946. Link Code

Conference

Y Zhao, X Luo, W Ju, C Chen, XS Hua, M Zhang. Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting[C]//2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023: 2303-2316. Link
Zikang Zhou, Jianping Wang, Yung-Hui Li, Yu-Kai Huang. Query-Centric Trajectory Prediction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). CVPR, 2023. Link Code
Fang Y, Qin Y, Luo H, et al. When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks[C]//2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023: 517-529. Link Code
Zhang Q, Huang C, Xia L, et al. Automated Spatio-Temporal Graph Contrastive Learning[C]//Proceedings of the ACM Web Conference 2023. 2023: 295-305. Link Code
Li Z, Ren Q, Chen L, et al. Dual-Stage Graph Convolution Network With Graph Learning For Traffic Prediction[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5. Link
Wang B, Zhang Y, Wang P, et al. A Knowledge-Driven Memory System for Traffic Flow Prediction[C]//Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV. Cham: Springer Nature Switzerland, 2023: 192-207. Link
Liang H, Liu A, Qu J, et al. Region-Aware Graph Convolutional Network for Traffic Flow Forecasting[C]//Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV. Cham: Springer Nature Switzerland, 2023: 431-446. Link
Jiang R, Wang Z, Yong J, et al. Spatio-Temporal Meta-Graph Learning for Traffic Forecasting[C]. AAAI 2023. Link Code
Ji J, Wang J, Huang C, et al. Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction[C]. AAAI 2023. Link Code

Preprint

Li Z, Li W, Hwang K. Adaptive Graph Convolution Networks for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2307.05517, 2023. Link Code
Luo X, Zhu C, Zhang D, et al. STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction[J]. arXiv preprint arXiv:2307.00495, 2023. Link Code
Gupta M, Kodamana H, Ranu S. FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks[J]. arXiv preprint arXiv:2306.08277, 2023. Link Code
Liu X, Xia Y, Liang Y, et al. LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting[J]. arXiv preprint arXiv:2306.08259, 2023. Link Code and Data
Caicedo J D, González M C, Walker J L. Public Transit Demand Prediction During Highly Dynamic Conditions: A Meta-Analysis of State-of-the-Art Models and Open-Source Benchmarking Infrastructure[J]. arXiv preprint arXiv:2306.06194, 2023. Link Code Data
Chen L, Chai D, Wang L. UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction[J]. arXiv preprint arXiv:2306.04144, 2023. Link Code
Liang G, Tiwari P, Nowaczyk S, et al. Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting[J]. arXiv preprint arXiv:2305.09703, 2023. Link Code
Choi J, Park N. Graph Neural Rough Differential Equations for Traffic Forecasting[J]. arXiv preprint arXiv:2303.10909, 2023. Link Code
Huang B, Hooi B, Shu K. TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks[J]. arXiv preprint arXiv:2304.08640, 2023. Link Code
Choi J, Park N. Graph Neural Rough Differential Equations for Traffic Forecasting[J]. arXiv preprint arXiv:2303.10909, 2023. Link Code
Liu H, Zhu C, Zhang D, et al. Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting[J]. arXiv preprint arXiv:2302.12973, 2023. Link
Chen K, Han J, Feng S, et al. Cross-City Traffic Prediction via Semantic-Fused Hierarchical Graph Transfer Learning[J]. arXiv preprint arXiv:2302.11774, 2023. Link
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2022

Journal

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Xia M, Jin D, Chen J. Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
Lai Q, Tian J, Wang W, et al. Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
Yang J, Xie F, Yang J, et al. Spatial-temporal correlated graph neural networks based on neighborhood feature selection for traffic data prediction[J]. Applied Intelligence, 2022: 1-16. Link
Zhang R, Xie F, Sun R, et al. Spatial-temporal dynamic semantic graph neural network[J]. Neural Computing and Applications, 2022: 1-14. Link
Zhang S, Liu Y, Xiao Y, et al. Spatial-Temporal Upsampling Graph Convolutional Network for daily long-term traffic speed prediction[J]. Journal of King Saud University-Computer and Information Sciences, 2022. Link Code
Yang S, Li H, Luo Y, et al. Spatiotemporal Adaptive Fusion Graph Network for Short-Term Traffic Flow Forecasting[J]. Mathematics, 2022, 10(9): 1594. Link
Dong C, Zhang K, Wei X, et al. Spatiotemporal Graph Attention Network modeling for multi-step passenger demand prediction at multi-zone level[J]. Physica A: Statistical Mechanics and its Applications, 2022: 127789. Link
Wang Y, Jing C. Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting[J]. ISPRS International Journal of Geo-Information, 2022, 11(2): 102. Link
Li J, Wu P, Li R, et al. ST-CRMF: Compensated Residual Matrix Factorization with Spatial-Temporal Regularization for Graph-Based Time Series Forecasting[J]. Sensors, 2022, 22(15): 5877. Link
Ni Q, Zhang M. STGMN: A gated multi-graph convolutional network framework for traffic flow prediction[J]. Applied Intelligence, 2022: 1-14. Link
Gu Y, Deng L. STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting[J]. Mathematics, 2022, 10(9): 1599. 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
Ou J, Sun J, Zhu Y, et al. STP-TrellisNets+: Spatial-Temporal Parallel TrellisNets for Multi-Step Metro Station Passenger Flow Prediction[J]. IEEE Transactions on Knowledge and Data Engineering, 2022. Link
Zhou J, Qin X, Yu K, et al. STSGAN: Spatial-Temporal Global Semantic Graph Attention Convolution Networks for Urban Flow Prediction[J]. ISPRS International Journal of Geo-Information, 2022, 11(7): 381. Link
Wang B, Wang J. ST-MGAT: Spatio-temporal multi-head graph attention network for Traffic prediction[J]. Physica A: Statistical Mechanics and its Applications, 2022: 127762. Link
Liao W, Zeng B, Liu J, et al. Taxi demand forecasting based on the temporal multimodal information fusion graph neural network[J]. Applied Intelligence, 2022: 1-14. Link
Zhang W, Yan S, Li J. TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality[J]. Information Sciences, 2022, 608: 718-733. Link
Khaled A, Elsir A M T, Shen Y. TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network[J]. Knowledge-Based Systems, 2022: 108990. Link
Mai W, Chen J, Chen X. Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction[J]. Applied Sciences, 2022, 12(6): 2842. Link
Liu X, Zhang Z, Lyu L, et al. Traffic Anomaly Prediction Based on Joint Static-Dynamic Spatio-Temporal Evolutionary Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2022. Link Code
Luan S, Ke R, Huang Z, et al. Traffic congestion propagation inference using dynamic Bayesian graph convolution network[J]. Transportation Research Part C: Emerging Technologies, 2022, 135: 103526. Link Code
Chen L, Shi P, Li G, et al. Traffic flow prediction using multi-view graph convolution and masked attention mechanism[J]. Computer Communications, 2022. Link
Han S Y, Sun Q W, Zhao Q, et al. Traffic Forecasting Based on Integration of Adaptive Subgraph Reformulation and Spatio-Temporal Deep Learning Model[J]. Electronics, 2022, 11(6): 861. Link
Wang J, Wang W, Liu X, et al. Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network[J]. Physica A: Statistical Mechanics and its Applications, 2022, 590: 126736. Link
Wang Y, Zheng J, Du Y, et al. Traffic-GGNN: Predicting Traffic Flow via Attentional Spatial-Temporal Gated Graph Neural Networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
Wang T, Ni S, Qin T, et al. TransGAT: A Dynamic Graph Attention Residual Networks for Traffic Flow Forecasting[J]. Sustainable Computing: Informatics and Systems, 2022: 100779. Link
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Conference

Yuan K, Liu J, Lou J. Higher-Order Masked Graph Neural Networks for Traffic Flow Prediction[C]//2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022: 1305-1310. Link Code
Chen T, Nie L, Pan J, et al. Origin-Destination Traffic Prediction based on Hybrid Spatio-Temporal Network[C]//2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022: 879-884. Link Code
Fei Y, Hu M, Wei X, et al. Orthogonal Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting[C]//2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022: 71-76. Link
Jiang S, Wang Q, Wang C, et al. Flow Prediction via Multi-view Spatial-Temporal Graph Neural Network[C]//Data Mining and Big Data: 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part I. Singapore: Springer Nature Singapore, 2023: 77-92. Link
Nguyen M P, Dao M S, Nguyen T B. 3D-STGPCN: 3D Spatio-Temporal Graph Point-wise Convolutional Network for Traffic Forecasting[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 5998-6007. Link Code
He H, Ye K. Graph Structure Neural Differential Equations on Spatio-temporal Prediction[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 1830-1835. Link Code
Yao H X, Guo J, Yang J T, et al. Dynamic Graph Attention Recurrent Network for Traffic Prediction[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 1254-1259. Link
Yao H, Chen R, Xie Z, et al. MRA-DGCN: Multi-Range Attention-Based Dynamic Graph Convolutional Network for Traffic Prediction[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 1613-1621. Link
Cai M, Pang Y, Sekimoto Y. Spatial Attention Based Grid Representation Learning For Predicting Origin–Destination Flow[C]//2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022: 485-494. Link
Li D, Kwak S, Geroliminis N. TwoResNet: Two-level resolution neural network for traffic forecasting on freeway networks[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 3963-3969. Link Code
Hermes L, Hammer B, Melnik A, et al. A Graph-based U-Net Model for Predicting Traffic in unseen Cities[C]. 2022 International Joint Conference on Neural Networks (IJCNN), 2022. Link Code
Lee H, Jin S, Chu H, et al. Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting[C]. International Conference on Learning Representations (ICLR 2022). Link Code
Liu X, Liang Y, Huang C, et al. When do contrastive learning signals help spatio-temporal graph forecasting?[C]//Proceedings of the 30th International Conference on Advances in Geographic Information Systems. 2022: 1-12. Link Code
Feng Y, Han F, Zhao S. A Graph Convolutional Stacked Temporal Attention Neural Network for Traffic Flow Forecasting[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1-7. Link
Dong H, Zhu P, Gao J, et al. A Short-term Traffic Flow Forecasting Model Based on Spatial-temporal Attention Neural Network[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 416-421. Link
Li S, Ge L, Lin Y, et al. Adaptive Spatial-Temporal Fusion Graph Convolutional Networks for Traffic Flow Forecasting[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1-8. Link
Hwang J, Noh B, Jin Z, et al. Asymmetric Long-Term Graph Multi-Attention Network for Traffic Speed Prediction[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 1498-1503. Link
Cao S, Wu L, Zhang R, et al. Capturing Local and Global Spatial-Temporal Correlations of Spatial-Temporal Graph Data for Traffic Flow Prediction[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1-8. Link
Zhang L, Fu K, Ji T, et al. Granger Causal Inference for Interpretable Traffic Prediction[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 1645-1651. Link
Chen B, Hu K, Li Y, et al. Hybrid Spatio-Temporal Graph Convolution Network For Short-Term Traffic Forecasting[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 2128-2133. Link
Hu J, Lin X, Wang C. MGCN: Dynamic Spatio-Temporal Multi-Graph Convolutional Neural Network[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1-9. Link
Mehmood A, Khan T A, Muhammad A, et al. Multi-Class Traffic Density Forecasting in IoV using Spatio-Temporal Graph Neural Networks[C]//2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2022: 1-6. Link
Wang Q, He G, Lu P, et al. Spatial-Temporal Graph-Based Transformer Model for Traffic Flow Forecasting[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 2806-2811. Link
Katayama H, Yasuda S, Fuse T. Traffic Density Based Travel-Time Prediction With GCN-LSTM[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022: 2908-2913. Link
Das D. UApredictor: Urban Anomaly Prediction from Spatial-Temporal Data using Graph Transformer Neural Network[C]//2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022: 1-8. Link
Feng A, Tassiulas L. Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 3933-3937. Link
Li F, Yan H, Jin G, et al. Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 1084-1093. Link
Wang Y, Ren Q. Dynamic Graph Convolutional Network for Long Short-term Traffic Flow Prediction[C]//2022 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2022: 1-6. Link
Li L, Bi J, Yang K, et al. MGC-GAN: Multi-Graph Convolutional Generative Adversarial Networks for Accurate Citywide Traffic Flow Prediction[C]. IEEE SMC, 2022. Link
Liu Z, Fu K, Liu X. Multi-view Cascading Spatial-Temporal Graph Neural Network for Traffic Flow Forecasting[C]//International Conference on Artificial Neural Networks. Springer, Cham, 2022: 605-616. Link
Song J, Son J, Seo D, et al. ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 4500-4504. Link
Kim D, Cho Y, Kim D, et al. Residual Correction in Real-Time Traffic Forecasting[C]. CIKM, 2022. Link
Li G, Wang X, Njoo G S, et al. A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction[C]. IEEE International Conference on Data Engineering (ICDE), 2022. Link
Shen Y, Li L, Xie Q, et al. A Two-Tower Spatial-Temporal Graph Neural Network for Traffic Speed Prediction[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2022: 406-418. Link
Sun J, Li J, Wu C, et al. Ada-STNet: A Dynamic AdaBoost Spatio-Temporal Network for Traffic Flow Prediction[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 5478-5482. Link
Xie Y, Xiong Y, Zhu Y, et al. Concurrent Transformer for Spatial-Temporal Graph Modeling[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2022: 314-321. Link
Shao Z, Zhang Z, Wei W, et al. Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting[J]. International Conference on Very Large Databases (VLDB), 2022. Link Code
Lan S, Ma Y, Huang W, et al. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting[C]//International Conference on Machine Learning. PMLR, 2022: 11906-11917. Link Code
Wang Z, Jiang R, Xue H, et al. Event-Aware Multimodal Mobility Nowcasting[C]. AAAI, 2022. Link Code
Rao X, Wang H, Zhang L, et al. FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting[C]//Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2022. Link Code
Choi J, Choi H, Hwang J, et al. Graph Neural Controlled Differential Equations for Traffic Forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence. 2022. Link Code
Zhang C, Zhang S, Yu S, et al. Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning[C]//2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2022: 2041-2046. Link
Liu D, Wang J, Shang S, et al. MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022: 1042-1050. Link Code
Li P, Fang J, Chao P, et al. JS-STDGN: A Spatial-Temporal Dynamic Graph Network Using JS-Graph for Traffic Prediction[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2022: 191-206. Link
Stromann O, Razavi A, Felsberg M. Learning to integrate vision data into road network data[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 4548-4552. Link
Semenova N, Porvatov V, Tishin V, et al. Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation[C]. PKDD, 2022. Link Code
Shao W, Jin Z, Wang S, et al. Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention[C]. The 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), 2022. Link
Li R, Zhong T, Jiang X, et al. Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022: 936-944. Link
Shao Z, Zhang Z, Wang F, et al. Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting[C]. KDD, 2022. Link Code
He Q, Dong Z, Chen F, et al. Pyramid: Enabling hierarchical neural networks with edge computing[C]//Proceedings of the ACM Web Conference 2022. 2022: 1860-1870. Link
Yu H, Li T, Yu W, et al. Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting[C]. IJCAI, 2022. Link Code
Lu B, Gan X, Zhang W, et al. Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer[C]. KDD, 2022. Link Code
Tang J, Qian T, Liu S, et al. Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting[C]. IJCNN, 2022. Link
Ji J, Wang J, Jiang Z, et al. STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction[J]. AAAI, 2022. Link Code
Chen Y, Segovia-Dominguez I, Coskunuzer B, et al. TAMP-S2GCNets: coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting[C]//International Conference on Learning Representations. 2022. Link Code
Tabatabaie M, Maniscalco J, Lynch C, et al. Towards Spatio-Temporal Cross-Platform Graph Embedding Fusion for Urban Traffic Flow Prediction[C]. UrbComp, 2022. Link
Xue Y, Fan X, Huang Y, et al. Traffic Forecasting Model Based on Two-stage Stacked Graph Convolution Network[C]//2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2022: 1089-1094. Link
Zhuang D, Wang S, Koutsopoulos H, et al. Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022: 4639-4647. Link

Preprint

Tang Y, He J, Zhao Z. HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction[J]. arXiv preprint arXiv:2210.07765, 2022. Link Code
Deng L, Wu C, Lian D, et al. Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting[J]. arXiv preprint arXiv:2211.00641, 2022. Link Code
Tang Y, He J, Zhao Z. HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction[J]. arXiv preprint arXiv:2210.07765, 2022. Link Code
Liang Y, Huang G, Zhao Z. Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach[J]. arXiv preprint arXiv:2203.10961, 2022. Link
Liang Y, Huang G, Zhao Z. Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks[J]. arXiv preprint arXiv:2211.08903, 2022. Link
Lei B, Huang S, Ding C, et al. Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach[J]. arXiv preprint arXiv:2211.03033, 2022. Link
Shoman M, Aboah A, Daud A, et al. GC-GRU-N for Traffic Prediction using Loop Detector Data[J]. arXiv preprint arXiv:2211.08541, 2022. Link
Miao Y, Xu Y, Mandic D. Hyper-GST: Predict Metro Passenger Flow Incorporating GraphSAGE, Hypergraph, Social-meaningful Edge Weights and Temporal Exploitation[J]. arXiv preprint arXiv:2211.04988, 2022. Link
Tian Y, Liu Z, Qu Y. M3FGM: a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction[J]. arXiv preprint arXiv:2210.16193, 2022. Link
Cini A, Marisca I, Bianchi F M, et al. Scalable Spatiotemporal Graph Neural Networks[J]. arXiv preprint arXiv:2209.06520, 2022. Link
He S, Luo Q, Du R, et al. STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph[J]. arXiv preprint arXiv:2210.16789, 2022. Link
Shin Y, Yoon Y. PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting[J]. arXiv preprint arXiv:2202.08982, 2022. Link Code
Pu Y. Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction[J]. arXiv preprint arXiv:2210.00704, 2022. Link Code
Zhao L, Chen M, Du Y, et al. Spatial-Temporal Graph Convolutional Gated Recurrent Network for Traffic Forecasting[J]. arXiv preprint arXiv:2210.02737, 2022. Link Code
Luo R, Song Y, Huang L, et al. STGIN: A Spatial Temporal Graph-Informer Network for Long Sequence Traffic Speed Forecasting[J]. arXiv preprint arXiv:2210.01799, 2022. Link
Roudbari N S, Patterson Z, Eicker U, et al. Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction[J]. arXiv preprint arXiv:2209.03858, 2022. Link
Chen Z, Lu Z, Chen Q, et al. A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism[J]. arXiv preprint arXiv:2205.04762, 2022. Link
Feng A, Tassiulas L. Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2207.05064, 2022. Link
Kubota Y, Ohira Y, Shimizu T. Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Prediction[J]. arXiv preprint arXiv:2203.00489, 2022. Link
Jin G, Li F, Zhang J, et al. Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction[J]. arXiv preprint arXiv:2207.10830, 2022. Link Code
Liang Y, Huang G, Zhao Z. Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach[J]. arXiv preprint arXiv:2203.10961, 2022. Link
Han L, Ma X, Sun L, et al. Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction[J]. arXiv preprint arXiv:2206.15005, 2022. Link Code
Mallick T, Balaprakash P, Macfarlane J. Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting[J]. arXiv preprint arXiv:2204.01618, 2022. Link
Jiang J, Wu B, Chen L, et al. Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting[J]. arXiv preprint arXiv:2208.03063, 2022. Link Code
Zhang R, Han L, Liu B, et al. Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction[J]. arXiv preprint arXiv:2205.14593, 2022. Link Code
Li M, Tang Y, Ma W. Few-Shot Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases[J]. arXiv preprint arXiv:2203.03965, 2022. Link Code
Li H, Zhang J, Yang L, et al. STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems[J]. arXiv preprint arXiv:2202.06727, 2022. Link
Chen W, Wang Y, Du C, et al. Learning Sparse and Continuous Graph Structures for Multivariate Time Series Forecasting[J]. arXiv preprint arXiv:2201.09686, 2022. Link Code
Ma Y, Lan S, Wang W, et al. Modeling of Spatial-Temporal Dependency in Traffic Flow Data for Traffic Forecasting[J]. Available at SSRN 4142192. Link Code
Jin M, Zheng Y, Li Y F, et al. Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs[J]. arXiv preprint arXiv:2202.08408, 2022. Link
Satorras V G, Rangapuram S S, Januschowski T. Multivariate Time Series Forecasting with Latent Graph Inference[J]. arXiv preprint arXiv:2203.03423, 2022. Link
Yin X, Li F, Shen Y, et al. NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction[J]. arXiv preprint arXiv:2207.01301, 2022. Link
Huang J, Huang B, Yu W, et al. ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario[J]. arXiv preprint arXiv:2208.08218, 2022. Link
Rodrigues F. On the importance of stationarity, strong baselines and benchmarks in transport prediction problems[J]. arXiv preprint arXiv:2203.02954, 2022. Link
Tuli S, Wilkinson M R, Kettell C. RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting[J]. arXiv preprint arXiv:2206.05602, 2022. Link
Zhao W, Zhang S, Zhou B, et al. Residual Graph Convolutional Recurrent Networks For Multi-step Traffic Flow Forecasting[J]. arXiv preprint arXiv:2205.01480, 2022. Link Code
Weikang C, Yawen L, Zhe X, et al. Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting[J]. arXiv preprint arXiv:2206.03128, 2022. Link
Liu A, Zhang Y. Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting[J]. arXiv preprint arXiv:2205.08689, 2022. Link
Xie P, Ma M, Li T, et al. Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction[J]. arXiv preprint arXiv:2204.02650, 2022. Link
Fang Y, Qin Y, Luo H, et al. Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network[J]. arXiv e-prints, 2021: arXiv: 2112.02740. Link
Zhao W, Zhang S, Zhou B, et al. STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction[J]. arXiv preprint arXiv:2203.10749, 2022. Link Code
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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
Xing D, Zhao C, Wang G. A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset[J]. arXiv preprint arXiv:2203.12505, 2022. Link
Zhao K, Xu M, Yang Z, et al. A Spatial–Temporal Similar Graph Attention Network for Cyber Physical System Perception via Traffic Forecasting[J]. Journal of Circuits, Systems and Computers, 2021: 2250112. 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
Qi T, Li G, Chen L, et al. ADGCN: An Asynchronous Dilation Graph Convolutional Network for Traffic Flow Prediction[J]. IEEE Internet of Things Journal, 2021. 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
Guo H, Zhang D, Jiang L, et al. ASTCN: An Attentive Spatial Temporal Convolutional Network for Flow Prediction[J]. IEEE Internet of Things Journal, 2021. Link
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
Yu K, Qin X, Jia Z, et al. Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction[J]. Sensors, 2021, 21(24): 8468. Link
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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
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Dapeng Z, Xiao F. Dynamic Auto-structuring Graph Neural Network: A Joint Learning Framework for Origin-Destination Demand Prediction[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. Link
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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
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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
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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
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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
Wang C, Zhang H, Yao S, et al. MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit[J]. Journal of Advanced Transportation, 2021, 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
Ye X, Fang S, Sun F, et al. Meta Graph Transformer: A Novel Framework for Spatial-Temporal Traffic Prediction[J]. Neurocomputing, 2021. Link Code
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 2022/03/01)
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
Liu S, Dai S, Sun J, et al. Multicomponent Spatial-Temporal Graph Attention Convolution Networks for Traffic Prediction with Spatially Sparse Data[J]. Computational Intelligence and Neuroscience, 2021, 2021. 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
Zeng Z, Zhao W, Qian P, et al. Robust Traffic Prediction From Spatial-Temporal Data Based on Conditional Distribution Learning[J]. IEEE Transactions on Cybernetics, 2021. Link
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
Wang P, Liu X, Wang Y, et al. Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication[J]. Applied Sciences, 2021, 11(23): 11530. 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
Hou F, Zhang Y, Fu X, et al. The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM[J]. Journal of Advanced Transportation, 2021, 2021. Link
Wang S, Zhang J, Li J, et al. Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal Networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. Link Code
Wang J, Wang W, Liu X, et al. Traffic prediction based on auto spatiotemporal Multi-graph Adversarial Neural Network[J]. Physica A: Statistical Mechanics and its Applications, 2021: 126736. 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
Wang X, Zhao J, Zhu L, et al. Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting[C]//GLOBECOM 2021-2021 IEEE Global Communications Conference. IEEE, 2021. Link
Zhang M, Li Y, Sun F, et al. Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction[C]//2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021: 1475-1480. 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
Chen Y, Mao J, Huang H, et al. Analysis of Different Graph Convolutional Network Prediction Models with Spatial Dependence Evaluation[C]//2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021: 1780-1785. 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
Xie X, Shao K, Wang Y, et al. Automated Type-Aware Traffic Speed Prediction based on Sparse Intelligent Camera System[C]//2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 4869-4874. Link
Liu H, Wu Q, Zhuang F, et al. Community-aware multi-task transportation demand prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(1): 320-327. Link
Chen H, Rossi R A, Mahadik K, et al. Context Integrated Relational Spatio-Temporal Resource Forecasting[C]//2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021: 1359-1368. 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
Liu M, Du B, Sun L. Co-Prediction of Multimodal Transportation Demands With Self-learned Spatial Dependence[C]//2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021: 824-833. 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
Li H, Zhang S, Li X, et al. DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems. 2021: 133-136. 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
Zhao H, Yang H, Wang Y, et al. Domain-Adversarial-based Temporal Graph Convolutional Network for Traffic Flow Prediction Problem[C]//2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021: 1365-1370. Link
Wu I Y, Lin F, Hsieh H P. Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems. 2021: 554-563. Link
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
Golovnin O, Perevozchikov N. E-STGCN: Enhanced Spatial-Temporal Graph Convolutional Network for Road Traffic Forecasting[C]//2021 International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2021: 1-4. 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
Ruan H, Feng X, Zheng H. Graph Transformer Attention Networks for Traffic Flow Prediction[C]//2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021: 1778-1782. 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
Yang J, Liu T, Li C, et al. MGSTCN: A Multi-Graph Spatio-Temporal Convolutional Network for Metro Passenger Flow Prediction[C]//2021 7th International Conference on Big Data Computing and Communications (BigCom). IEEE, 2021: 164-171. Link
Mao J, Huang H, Chen Y, et al. Mining the Graph Representation of Traffic Speed Data for Graph Convolutional Neural Network[C]//2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021: 1205-1210. Link
Piovesan N, De Domenico A, López-Pérez D, et al. Mobile Traffic Forecasting for Green 5G Networks[C]//2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021: 1-6. Link
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
He H, Ye K, Xu C Z. Multi-feature Urban Traffic Prediction Based on Unconstrained Graph Attention Network[C]//2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021: 1409-1417. Link
Jing B, Tong H, Zhu Y. Network of tensor time series[C]//Proceedings of the Web Conference 2021. 2021: 2425-2437. 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
Chen Y, Jiang W, Fu H, et al. Spatio-Temporal Dynamic Multi-graph Attention Network for Ride-Hailing Demand Prediction[C]//International Conference on Neural Information Processing. Springer, Cham, 2021: 133-144. 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
Zhang H, Gan X, Fu L, et al. Spatiotemporal Graph Neural Network for Traffic Prediction Exploiting Cascading Behavior[C]//2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021: 01-06. Link
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
Hu S, Yu Z, Zhou D, et al. STOG: A Traffic Prediction Scheme Based on Spatio-Temporal Optimized Graph Neural Networks[C]//2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). IEEE, 2021: 1-5. 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
Ali M A, Venkatesan S, Liang V, et al. TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting[C]//2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021: 982-987. 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
Chen G, Xu X, Zheng H, et al. Traffic Forecasting with Adversarial Domain Adaptation in Edge-Computing Systems[C]//2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021: 1366-1370. Link
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
Huang Y, Song X, Zhang S, et al. Transfer Learning in Traffic Prediction with Graph Neural Networks[C]//2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021: 3732-3737. 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
Fang Y, Qin Y, Luo H, et al. CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting[J]. arXiv preprint arXiv:2112.02736, 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
Qin Y, Fang Y, Luo H, et al. DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting[J]. arXiv preprint arXiv:2112.02264, 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
Lin L, Li W, Zhu L. Network-wide multi-step traffic volume prediction using graph convolutional gated recurrent neural network[J]. arXiv preprint arXiv:2111.11337, 2021. Link
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
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
Wang Y, Xu D, Peng P, et al. An urban commuters’ OD hybrid prediction method based on big GPS data[J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020, 30(9): 093128. Link
James J Q. Citywide traffic speed prediction: A geometric deep learning approach[J]. Knowledge-Based Systems, 2020: 106592. Link
Han X, Shen G, Yang X, et al. Congestion recognition for hybrid urban road systems via digraph convolutional network[J]. Transportation Research Part C: Emerging Technologies, 2020, 121: 102877. 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
Yu L, Du B, Hu X, et al. Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction[J]. Neurocomputing, 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
Feng D, Wu Z, Zhang J, et al. Dynamic Global-Local Spatial-Temporal Network for Traffic Speed Prediction[J]. IEEE Access, 2020, 8: 209296-209307. Link
Guo K, Hu Y, Qian Z, et al. Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link Code
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
Chen K, Chen F, Lai B, et al. Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction[J]. IEEE Access, 2020, 8: 185136-185145. 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
Ali A, Zhu Y, Zakarya M. Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction[J]. Neural networks, 2021. Link
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
Zhou Z, Wang Y, Xie X, et al. Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. 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
He K, Chen X, Wu Q, et al. Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction[J]. IEEE Transactions on Mobile Computing, 2020. Link
Zhang K, He F, Zhang Z, et al. Graph attention temporal convolutional network for traffic speed forecasting on road networks[J]. Transportmetrica B: Transport Dynamics, 2020: 1-19. 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
Li C, Bai L, Liu W, et al. Graph Neural Network for Robust Public Transit Demand Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link
Liu T, Jiang A, Miao X, et al. Graph-based Dynamic Modeling and Traffic Prediction of Urban Road Network[J]. IEEE Sensors Journal, 2021. 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 Code
Liu J, Ong G P, Chen X. GraphSAGE-Based Traffic Speed Forecasting for Segment Network With Sparse Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. 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
Shin Y, Yoon Y. Incorporating Dynamicity of Transportation Network With Multi-Weight Traffic Graph Convolutional Network for Traffic Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link
Bing H, Zhifeng X, Yangjie X, et al. Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data[J]. Complexity, 2020, 2020. Link
Lewenfus G, Martins W A, Chatzinotas S, et al. Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning[J]. IEEE Transactions on Signal and Information Processing over Networks, 2020. 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
Fang S, Pan X, Xiang S, et al. Meta-MSNet: Meta-Learning based Multi-Source Data Fusion for Traffic Flow Prediction[J]. IEEE Signal Processing Letters, 2020. Link
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
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
Yin X, Wu G, Wei J, et al. Multi-Stage Attention Spatial-Temporal Graph Networks for Traffic Prediction[J]. Neurocomputing, 2020. Link
Gong Y, Li Z, Zhang J, et al. Online Spatio-temporal Crowd Flow Distribution Prediction for Complex Metro System[J]. IEEE Transactions on Knowledge and Data Engineering, 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
Chu J, Wang X, Qian K, et al. Passenger demand prediction with cellular footprints[J]. IEEE Transactions on Mobile Computing, 2020. Link
Liu L, Chen J, Wu H, et al. Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link Code with data
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
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
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
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
Kong X, Xing W, Wei X, et al. STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting[J]. IEEE Access, 2020. Link Code
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
Qiu H, Zheng Q, Msahli M, et al. Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2020. Link Code
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
Sun X, Li J, Lv Z, et al. Traffic Flow Prediction Model Based on Spatio-Temporal Dilated Graph Convolution[J]. KSII Transactions on Internet & Information Systems, 2020, 14(9). Link
Li W, Wang X, Zhang Y, et al. Traffic Flow Prediction over Muti-Sensor Data Correlation with Graph Convolution Network[J]. Neurocomputing, 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
Shen Y, Jin C, Hua J. TTPNet: A Neural Network for Travel Time Prediction Based on Tensor Decomposition and Graph Embedding[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. Link Code
Wang Y, Fang S, Zhang C, et al. TVGCN: Time-Variant Graph Convolutional Network for Traffic Forecasting[J]. Neurocomputing, 2021. 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
Zhang Y, Lu M, Li H. Urban Traffic Flow Forecast Based on FastGCRNN[J]. Journal of Advanced Transportation, 2020, 2020. 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

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
Guopeng L I, Knoop V L, van Lint H. Dynamic Graph Filters Networks: A Gray-box Model for Multistep Traffic Forecasting[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020: 1-6. Link Code
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
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
Zhang S, Zheng H, Su H, et al. GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series[C]//2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. Link
Sánchez C S, Wieder A, Sottovia P, et al. GANNSTER: Graph-Augmented Neural Network Spatio-Temporal Reasoner for Traffic Forecasting[C]//International Workshop on Advanced Analysis and Learning on Temporal Data. 2020. Link Code (empty till 2022/03/01)
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 2022/03/01)
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
Li H, Zhang S, Su L, et al. GraphSANet: A Graph Neural Network and Self Attention Based Approach for Spatial Temporal Prediction in Sensor Network[C]//2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020: 5756-5758. Link
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
Xi G, Yin L, Liu K. Intra-urban Region-based Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network Enhanced by Spatial Context[C]//The 10th International Workshop on Urban Computing (UrbComp). 2021. 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
Li H, Jin D, Li X, et al. Multi-Task Synchronous Graph Neural Networks for Traffic Spatial-Temporal Prediction[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems. 2021: 137-140. 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
Lau Y H, Wong R C W. Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting: Spatial Layers First or Temporal Layers First?[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems. 2021: 427-430. 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 Code
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 2022/03/01)
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
Xu Y, Paliwal M, Zhao X. Real-time Forecasting of Dockless Scooter-Sharing Demand: A Context-Aware Spatio-Temporal Multi-Graph Convolutional Network Approach[J]. arXiv preprint arXiv:2111.01355, 2021. 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 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