Advertisement: We would like to cordially invite you to submit a paper to our special session on "Graph Neural Network for Traffic Forecasting" for the 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022).
- Special session code: 6495a
- ITSC 2022 website: https://www.ieee-itsc2022.org/
- Paper submission website: http:https://its.papercept.net/
- Deadline for manuscript submissions: 15 March 2022.
Advertisement: We would like to cordially invite you to submit a paper to our special issue on "Graph Neural Network for Traffic Forecasting" for Algorithms (EI and ESCI-indexed, ISSN 1999-4893).
- Special issue website: https://www.mdpi.com/journal/algorithms/special_issues/Traffic_Forecasting
- Deadline for manuscript submissions: 1 December 2022.
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
For a wider collection of deep learning for traffic forecasting, you may check: DL4Traffic
Advertisement: If you are interested in maintaining this repository, feel free to drop me an email. Collaboration with Tsinghua University is always welcome, too.
Some simple paper statistics results are as follows.
Paper year count:
Top conferences with paper counts:
Top journals with paper counts:
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Deep Learning Time Series Forecasting Link
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A collection of research on spatio-temporal data mining Link
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Some TrafficFlowForecasting Solutions Link
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Urban-computing-papers Link
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Spatio-Temporal-papers Link
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Awesome-Mobility-Machine-Learning-Contents Link
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Traffic Prediction Link
- 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
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Zhao J, Chen C, Liao C, et al. 2F-TP: Learning Flexible Spatiotemporal Dependency for Flexible Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Zheng G, Chai W K, Katos V. A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks[J]. Expert Systems with Applications, 2022: 116585. Link
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Huang F, Yi P, Wang J, et al. A Dynamical Spatial-Temporal Graph Neural Network for Traffic Demand Prediction[J]. Information Sciences, 2022. Link
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Jin J, Rong D, Zhang T, et al. A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Xu D, Lin Z, Zhou L, et al. A GATs-GAN framework for road traffic states forecasting[J]. Transportmetrica B: Transport Dynamics, 2022: 1-13. Link
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Shang P, Yu C, Liu X, et al. A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network[J]. Digital Signal Processing, 2022: 103419. Link
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Liu F, Wang J, Tian J, et al. A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Diao C, Zhang D, Liang W, et al. A Novel Spatial-Temporal Multi-Scale Alignment Graph Neural Network Security Model for Vehicles Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Li Y, Zhao W, Fan H. A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction[J]. Mathematics, 2022, 10(10): 1754. Link
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Chen L, Shao W, Lv M, et al. AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Jin G, Liu C, Xi Z, et al. Adaptive Dual-View WaveNet for Urban Spatial-temporal Event Prediction[J]. Information Sciences, 2021. Link
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Xu H, Zou T, Liu M, et al. Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Ta X, Liu Z, Hu X, et al. Adaptive Spatio-temporal Graph Neural Network for traffic forecasting[J]. Knowledge-Based Systems, 2022: 108199. Link Code
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Zhao S, Li X. An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction[J]. Journal of Advanced Transportation, 2022, 2022. Link
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Liao L, Hu Z, Zheng Y, et al. An improved dynamic Chebyshev graph convolution network for traffic flow prediction with spatial-temporal attention[J]. Applied Intelligence, 2022: 1-13. Link
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Zhao J, Liu Z, Sun Q, et al. Attention-based Dynamic Spatial-Temporal Graph Convolutional Networks for Traffic Speed Forecasting[J]. Expert Systems with Applications, 2022: 117511. Link
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Zhang C, Zhou H Y, Qiu Q, et al. Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting[J]. ISPRS International Journal of Geo-Information, 2022, 11(2): 88. Link Code
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Jiang W, Xiao Y, Liu Y, et al. Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network[J]. Journal of Advanced Transportation, 2022, 2022. Link
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Zhao T, Huang Z, Tu W, et al. Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction[J]. Computers, Environment and Urban Systems, 2022, 94: 101776. Link
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Li F, Feng J, Yan H, et al. Crowd Flow Prediction for irregular Regions with Semantic Graph Attention Network[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2022. Link
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Lee K, Rhee W. DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103466. Link Code
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Zheng Q, Zhang Y. DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting[J]. IEEE Transactions on Big Data, 2022. Link
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Han S Y, Zhao Q, Sun Q W, et al. EnGS-DGR: Traffic Flow Forecasting with Indefinite Forecasting Interval by Ensemble GCN, Seq2Seq, and Dynamic Graph Reconfiguration[J]. Applied Sciences, 2022, 12(6): 2890. Link
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Luo G, Zhang H, Yuan Q, et al. ESTNet: Embedded Spatial-Temporal Network for Modeling Traffic Flow Dynamics[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Kong X, Wang K, Hou M, et al. Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Zou F, Ren Q, Tian J, et al. Expressway Speed Prediction Based on Electronic Toll Collection Data[J]. Electronics, 2022, 11(10): 1613. Link
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Yuan X, Chen J, Yang J, et al. FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Zhang X, Xu Y, Shao Y. Forecasting traffic flow with spatial–temporal convolutional graph attention networks[J]. Neural Computing and Applications, 2022: 1-23. Link
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Liu C, Xiao Z, Wang D, et al. Foreseeing private car transfer between urban regions with multiple graph-based generative adversarial networks[J]. World Wide Web, 2022: 1-20. Link
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Su J, Ren J, Yang J, et al. Gdformer: A Graph Diffusing Attention Based Approach for Traffic Flow Prediction[J]. Pattern Recognition Letters, 2022. Link Code (Still empty on 2022/03/01)
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Shi M, Tang Y, Zhu X, et al. Genetic-GNN: Evolutionary architecture search for Graph Neural Networks[J]. Knowledge-Based Systems, 2022, 247: 108752. Link Code
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James J Q. Graph Construction for Traffic Prediction: A Data-Driven Approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Geng X, He X, Xu L, et al. Graph Correlated Attention Recurrent Neural Network for Multivariate Time Series Forecasting[J]. Information Sciences, 2022. Link
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Lu Z, Lv W, Xie Z, et al. Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2022, 13(2): 1-24. Link
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Wang H, Zhang R, Cheng X, et al. Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link Code
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Dai F, Cao P, Huang P, et al. Hybrid deep learning approach for traffic speed prediction[J]. Big data, 2022. Link
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Jiang M, Li C, Li K, et al. Inter-Block Flow Prediction with Relation Graph Network for Cold-start on Bike-Sharing System[J]. IEEE Internet of Things Journal, 2022. Link
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Liang Y, Huang G, Zhao Z. Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach[J]. Transportation Research Part C: Emerging Technologies, 2022. Link
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Xu M, Li X, Wang F, et al. Learning to effectively model spatial-temporal heterogeneity for traffic flow forecasting[J]. World Wide Web, 2022: 1-17. Link
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Lu B, Gan X, Jin H, et al. Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2022, 13(2): 1-25. Link
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Qin Y, Zhao F, Fang Y, et al. Memory attention enhanced graph convolution long short‐term memory network for traffic forecasting[J]. International Journal of Intelligent Systems, 2022. Link
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Cui Z, Zhang J, Noh G, et al. MFDGCN: Multi-Stage Spatio-Temporal Fusion Diffusion Graph Convolutional Network for Traffic Prediction[J]. Applied Sciences, 2022, 12(5): 2688. Link
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Yin D, Jiang R, Deng J, et al. MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction[J]. GeoInformatica, 2022: 1-29. Link Code
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Yang L, Jiang S, Zhang F. Multitask Learning with Graph Neural Network for Travel Time Estimation[J]. Computational Intelligence and Neuroscience, 2022, 2022. Link
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Wang S, Zhang M, Miao H, et al. Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2022, 13(3): 1-22. Link
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Ma J, Chan J, Rajasegarar S, et al. Multi-attention graph neural networks for city-wide bus travel time estimation using limited data[J]. Expert Systems with Applications, 2022: 117057. Link
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Zhao C, Li X, Shao Z, et al. Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction[J]. Connection Science, 2022, 34(1): 1252-1272. Link
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Sun Y, Jiang G, Lam S K, et al. Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency[J]. Applied Soft Computing, 2022: 108977. Link
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Huang X, Ye Y, Yang X, et al. Multi-step Coupled Graph Convolution with Temporal-Attention for Traffic Flow Prediction[J]. IEEE Access, 2022. Link
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Wang J, Zhang Y, Wang L, et al. Multitask Hypergraph Convolutional Networks: A Heterogeneous Traffic Prediction Framework[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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Huang W, Cao B, Li X, et al. Passenger Flow Prediction for Public Transportation Stations Based on Spatio-Temporal Graph Convolutional Network with Periodic Components[J]. Journal of Circuits, Systems and Computers, 2022: 2250134. Link
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Xu Z, Hou L, Zhang Y, et al. Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model[J]. Sustainability, 2022, 14(6): 3295. Link
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Shin Y, Yoon Y. PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting[J]. arXiv preprint arXiv:2202.08982, 2022. Link Code
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Mi C, Cheng S, Lu F. Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks[J]. ISPRS International Journal of Geo-Information, 2022, 11(3): 185. Link
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Feng Y, Xu Y, Hu Q, et al. Predicting vacant parking space availability zone-wisely: a hybrid deep learning approach[J]. Complex & Intelligent Systems, 2022: 1-17. Link
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Nie X, Peng J, Wu Y, et al. Real-Time Traffic Speed Estimation for Smart Cities with Spatial Temporal Data: A Gated Graph Attention Network Approach[J]. Big Data Research, 2022: 100313. Link
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Yang H, Zhang X, Li Z, et al. Region-Level Traffic Prediction Based on Temporal Multi-Spatial Dependence Graph Convolutional Network from GPS Data[J]. Remote Sensing, 2022, 14(2): 303. Link Code
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Abdelraouf A, Abdel-Aty M, Mahmoud N. Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. Link
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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
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Ni Q, Zhang M. STGMN: A gated multi-graph convolutional network framework for traffic flow prediction[J]. Applied Intelligence, 2022: 1-14. Link
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Gu Y, Deng L. STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting[J]. Mathematics, 2022, 10(9): 1599. Link
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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
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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
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Yang T, Tang X, Liu R. Dual temporal gated multi-graph convolution network for taxi demand prediction[J]. Neural Computing and Applications, 2021: 1-16. Link
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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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Peng H, Du B, Liu M, et al. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning[J]. Information Sciences, 2021, 578: 401-416. Link
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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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Cho J H, Ham S W, Kim D K. Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems using a Graph Convolutional Network with Public Transit Usage Data[J]. Transportation Research Record, 2021: 03611981211012003. Link
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Liu C, Xiao Z, Wang D, et al. Exploiting Spatiotemporal Correlations of Arrive-Stay-Leave Behaviors for Private Car Flow Prediction[J]. IEEE Transactions on Network Science and Engineering, 2021. Link
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Zhang C, Zhang S, James J Q, et al. FASTGNN: A Topological Information Protected Federated Learning Approach For Traffic Speed Forecasting[J]. IEEE Transactions on Industrial Informatics, 2021. Link
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Lonare S, Bhramaramba R. Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network[J]. Journal of Shanghai Jiaotong University (Science), 2021: 1-9. Link
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Yang X, Zhu Q, Li P, et al. Fine-grained predicting urban crowd flows with adaptive spatio-temporal graph convolutional network[J]. Neurocomputing, 2021, 446: 95-105. Link
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