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.