https://github.com/jwwthu/GNN4Traffic
This is the repository for the collection of Graph Neural Network for Traffic Forecasting.
https://github.com/jwwthu/GNN4Traffic
Last synced: about 1 year ago
JSON representation
This is the repository for the collection of Graph Neural Network for Traffic Forecasting.
- Host: GitHub
- URL: https://github.com/jwwthu/GNN4Traffic
- Owner: jwwthu
- Created: 2020-04-23T07:40:40.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-08-07T11:55:53.000Z (almost 2 years ago)
- Last Synced: 2025-03-26T19:47:24.655Z (about 1 year ago)
- Size: 4.66 MB
- Stars: 1,123
- Watchers: 35
- Forks: 192
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://www.sciencedirect.com/science/article/pii/S0957417422011654)
* 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](https://www.mdpi.com/2571-5577/5/1/23)
* Jiang W. Bike sharing usage prediction with deep learning: a survey[J]. Neural Computing and Applications, 2022, 34(18): 15369-15385. [Link](https://link.springer.com/article/10.1007/s00521-022-07380-5)
* 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](https://www.mdpi.com/2220-9964/12/3/100)
For a wider collection of deep learning for traffic forecasting, you may check: [DL4Traffic](https://github.com/jwwthu/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](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](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](https://github.com/Alro10/deep-learning-time-series)
* A collection of research on spatio-temporal data mining [Link](https://github.com/xiepeng21/research_spatio-temporal-data-mining)
* Some TrafficFlowForecasting Solutions [Link](https://github.com/xiaoxiong74/TrafficFlowForecasting)
* Urban-computing-papers [Link](https://github.com/Knowledge-Precipitation-Tribe/Spatio-Temporal-papers)
* Awesome-Mobility-Machine-Learning-Contents [Link](https://github.com/zzsza/Awesome-Mobility-Machine-Learning-Contents/blob/master/README.md)
* Traffic Prediction [Link](https://github.com/aprbw/traffic_prediction)
* Paper & Code & Dataset Collection of Spatial-Temporal Data Mining. [Link](https://github.com/NickHan-cs/Spatio-Temporal-Data-Mining-Survey)
# Relevant Data Repositories
* Strategic Transport Planning Dataset [Link](https://github.com/nikita68/TransportPlanningDataset)
> 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](https://mediatum.ub.tum.de/doc/1638691/dwz10x0l0w38xdklv9zkrprqs.pdf)
* Zhang Y, Gong Q, Chen Y, et al. A Human Mobility Dataset Collected via LBSLab[J]. Data in Brief, 2023: 108898. [Link](https://www.sciencedirect.com/science/article/pii/S2352340923000161) [Data](https://doi.org/10.6084/m9.figshare.15000384.v3)
* 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](https://ieeexplore.ieee.org/abstract/document/10020886/) [Data](https://github.com/deepkashiwa20/DeepCrowd)
# 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](https://www.sciencedirect.com/science/article/pii/S1566253524001192)
* Fang S, Ji W, Xiang S, et al. PreSTNet: Pre-trained Spatio-Temporal Network for traffic forecasting[J]. Information Fusion, 2024, 106: 102241. [Link](https://www.sciencedirect.com/science/article/pii/S1566253524000198) [Code](https://github.com/WoodSugar/PreSTNet)
## 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](https://arxiv.org/abs/2401.00713)
# 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](https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12401)
* 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](https://link.springer.com/article/10.1007/s10489-023-04703-4)
* 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](https://www.sciencedirect.com/science/article/pii/S0952197623007996)
* 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](https://www.mdpi.com/2227-7390/11/13/2867)
* 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](https://www.mdpi.com/2079-9292/12/14/3158)
* 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](https://ieeexplore.ieee.org/abstract/document/10068539/)
* Zhao Z, Shen G, Zhou J, et al. Spatial-temporal hypergraph convolutional network for traffic forecasting[J]. PeerJ Computer Science, 2023, 9: e1450. [Link](https://peerj.com/articles/cs-1450/) [Code](http://dx.doi.org/10.7717/peerj-cs.1450#supplemental-information)
* 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](https://ieeexplore.ieee.org/abstract/document/10032116/) [Code](https://github.com/gorgen2020/SDGCN)
* 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](https://www.sciencedirect.com/science/article/pii/S002002552300854X) [Code](https://github.com/YanJieWen/STGMT-Tensorflow-implementation)
* 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](https://ieeexplore.ieee.org/abstract/document/10155190/)
* 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](https://ieeexplore.ieee.org/abstract/document/10145833/)
* 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](https://www.mdpi.com/2076-3417/13/12/7271)
* Ma C, Sun K, Chang L, et al. Enhanced Information Graph Recursive Network for Traffic Forecasting[J]. Electronics, 2023, 12(11): 2519. [Link](https://www.mdpi.com/2079-9292/12/11/2519)
* 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](https://www.sciencedirect.com/science/article/pii/S0020025523009052)
* 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](https://ieeexplore.ieee.org/abstract/document/10143385/)
* 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](https://www.sciencedirect.com/science/article/pii/S0957417423012265)
* 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](https://www.mdpi.com/2220-9964/12/6/241)
* 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](https://link.springer.com/article/10.1007/s12652-022-04431-6)
* 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](https://ieeexplore.ieee.org/abstract/document/10040629/)
* 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](https://www.mdpi.com/2076-3417/13/11/6796)
* 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](https://ieeexplore.ieee.org/abstract/document/10143176/)
* 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](https://www.sciencedirect.com/science/article/pii/S0950705123004264)
* Weng W, Fan J, Wu H, et al. A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting[J]. Pattern Recognition, 2023: 109670. [Link](https://www.sciencedirect.com/science/article/pii/S0031320323003710) [Code](https://github.com/wengwenchao123/DDGCRN)
* Corrias R, Gjoreski M, Langheinrich M. Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling[J]. Sensors, 2023, 23(10): 4803. [Link](https://www.mdpi.com/1424-8220/23/10/4803) [Code](https://github.com/corrir/Transformers-and-Graph-Convolutional-Networks-for-Human-Mobility-Modeling)
* Lablack M, Shen Y. Spatio-temporal graph mixformer for traffic forecasting[J]. Expert Systems with Applications, 2023, 228: 120281. [Link](https://www.sciencedirect.com/science/article/pii/S0957417423007832) [Code](https://github.com/mouradost/stgm)
* 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](https://www.tandfonline.com/doi/abs/10.1080/15472450.2023.2209913)
* 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](https://ieeexplore.ieee.org/abstract/document/10125071/)
* 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](https://www.emerald.com/insight/content/doi/10.1108/DTA-09-2022-0378/full/html)
* 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](https://www.mdpi.com/2071-1050/15/10/7903)
* 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](https://www.tandfonline.com/doi/abs/10.1080/13658816.2023.2203218)
* 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](https://journals.sagepub.com/doi/abs/10.1177/03611981221151024)
* 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](https://ieeexplore.ieee.org/abstract/document/10113777/)
* 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](https://www.mdpi.com/2071-1050/15/9/7696)
* 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](https://www.sciencedirect.com/science/article/pii/S0952197623006449)
* 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](https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12378)
* 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](https://www.sciencedirect.com/science/article/pii/S1566253523001537)
* 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](https://www.sciencedirect.com/science/article/pii/S0378437123003977)
* 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](https://ieeexplore.ieee.org/abstract/document/10139798/)
* 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](https://ieeexplore.ieee.org/abstract/document/10137765/)
* 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](https://www.mdpi.com/2076-3417/13/9/5625)
* 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](https://www.mdpi.com/2073-8994/15/5/995)
* 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](https://ieeexplore.ieee.org/abstract/document/10110344/)
* 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](https://www.sciencedirect.com/science/article/pii/S0957417423008953)
* 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](https://link.springer.com/article/10.1007/s11227-023-05383-0)
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* 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](https://www.sciencedirect.com/science/article/pii/S0950705123003416)
* 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](https://www.sciencedirect.com/science/article/pii/S0957417423008357)
* 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](https://www.sciencedirect.com/science/article/pii/S0378437123004685)
* 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](https://ieeexplore.ieee.org/abstract/document/10023949/) [Code](https://github.com/kaist-dmlab/MG-TAR)
* 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](https://www.sciencedirect.com/science/article/pii/S0952197623002282) [Code](https://github.com/Bounger2/ST-CGCN)
* 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](https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12365)
* 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](https://link.springer.com/article/10.1007/s11227-023-05261-9)
* 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](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283898)
* 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](https://dl.acm.org/doi/abs/10.1145/3586164)
* 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](https://journals.sagepub.com/doi/abs/10.1177/03611981231159407)
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* 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](https://ieeexplore.ieee.org/abstract/document/10101714/)
* 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](https://www.mdpi.com/1424-8220/23/8/3836)
* 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](https://link.springer.com/article/10.1007/s00521-023-08519-8)
* 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](https://ieeexplore.ieee.org/abstract/document/10093942/)
* 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](https://www.mdpi.com/2076-3417/13/8/4910)
* 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](https://www.sciencedirect.com/science/article/pii/S2046043023000357)
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* 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](https://ieeexplore.ieee.org/abstract/document/10023949/) [Code](https://github.com/kaist-dmlab/MG-TAR)
* 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](https://www.sciencedirect.com/science/article/pii/S0952197623002282) [Code](https://github.com/Bounger2/ST-CGCN)
* 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](https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12352)
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* 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](https://ieeexplore.ieee.org/abstract/document/10054349/)
* Zhang L, Ma J. A Spatiotemporal Graph Wavelet Neural Network for Traffic Flow Prediction[J]. Journal of Information and Intelligence, 2023. [Link](https://www.sciencedirect.com/science/article/pii/S2949715923000021)
* 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](https://ieeexplore.ieee.org/abstract/document/10042970/)
* 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](https://www.mdpi.com/2220-9964/12/4/144)
* Liu L, Cao Y, Dong Y. Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting[J]. Sustainability, 2023, 15(6): 4697. [Link](https://www.mdpi.com/2071-1050/15/6/4697)
* 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](https://www.sciencedirect.com/science/article/pii/S0004370223000450)
* 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](https://www.sciencedirect.com/science/article/pii/S0378437123002224)
* 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](https://dl.acm.org/doi/abs/10.1145/3586164)
* 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](https://www.mdpi.com/1424-8220/23/6/2897)
* 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](https://link.springer.com/article/10.1007/s10479-023-05260-2)
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## 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](https://ieeexplore.ieee.org/document/10184800)
* 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](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf) [Code](https://github.com/ZikangZhou/QCNet)
* 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](https://ieeexplore.ieee.org/abstract/document/10184591/) [Code](https://github.com/LMissher/STWave)
* 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](https://dl.acm.org/doi/abs/10.1145/3543507.3583304) [Code](https://github.com/HKUDS/AutoST)
* 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](https://ieeexplore.ieee.org/abstract/document/10095151/)
* 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](https://link.springer.com/chapter/10.1007/978-3-031-30678-5_15)
* 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](https://link.springer.com/chapter/10.1007/978-3-031-30678-5_32)
* Jiang R, Wang Z, Yong J, et al. Spatio-Temporal Meta-Graph Learning for Traffic Forecasting[C]. AAAI 2023. [Link](https://arxiv.org/abs/2211.14701) [Code](https://github.com/deepkashiwa20/MegaCRN)
* Ji J, Wang J, Huang C, et al. Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction[C]. AAAI 2023. [Link](https://arxiv.org/abs/2212.04475) [Code](https://github.com/Echo-Ji/ST-SSL)
## Preprint
* Li Z, Li W, Hwang K. Adaptive Graph Convolution Networks for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2307.05517, 2023. [Link](https://arxiv.org/abs/2307.05517) [Code](https://github.com/zhengdaoli/AGC-net)
* 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](https://arxiv.org/abs/2307.00495) [Code](https://github.com/trainingl/STG4Traffic)
* Gupta M, Kodamana H, Ranu S. FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks[J]. arXiv preprint arXiv:2306.08277, 2023. [Link](https://arxiv.org/abs/2306.08277) [Code](https://github.com/idea-iitd/Frigate)
* 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](https://arxiv.org/abs/2306.08259) [Code and Data](https://github.com/liuxu77/LargeST)
* 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](https://arxiv.org/abs/2306.06194) [Code](https://github.com/jdcaicedo251/transit_demand_prediction) [Data](https://datosabiertos-transmilenio.hub.arcgis.com/)
* 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](https://arxiv.org/abs/2306.04144) [Code](https://github.com/uctb/UCTB)
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* Choi J, Park N. Graph Neural Rough Differential Equations for Traffic Forecasting[J]. arXiv preprint arXiv:2303.10909, 2023. [Link](https://arxiv.org/abs/2303.10909) [Code](https://github.com/jeongwhanchoi/STG-NCDE)
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* Choi J, Park N. Graph Neural Rough Differential Equations for Traffic Forecasting[J]. arXiv preprint arXiv:2303.10909, 2023. [Link](https://arxiv.org/abs/2303.10909) [Code](https://github.com/jeongwhanchoi/STG-NCDE)
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* Nazzal M, Khreishah A, Lee J, et al. Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach[J]. arXiv preprint arXiv:2303.00524, 2023. [Link](https://arxiv.org/abs/2303.00524)
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* Zhao Y, Deng P, Liu J, et al. Causal conditional hidden Markov model for multimodal traffic prediction[J]. arXiv preprint arXiv:2301.08249, 2023. [Link](https://github.com/EternityZY/CCHMM)
* Deng P, Zhao Y, Liu J, et al. Spatio-temporal neural structural causal models for bike flow prediction[J]. arXiv preprint arXiv:2301.07843, 2023. [Link](https://arxiv.org/abs/2301.07843) [Code](https://github.com/EternityZY/STNSCM)
* Wen Y, Wang X, Xu W. Traffic Prediction Based on Spatiotemporal-Guided Multi Graph Sandwich-Transformer[J]. [Link](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4206935) [Code](https://github.com/YanJieWen/STGMT-Tensorflow-implementation)
* Liu X, Liang Y, Huang C, et al. Do We Really Need Graph Neural Networks for Traffic Forecasting?[J]. arXiv preprint arXiv:2301.12603, 2023. [Link](https://arxiv.org/abs/2301.12603)
* Zambon D, Alippi C. Where and How to Improve Graph-based Spatio-temporal Predictors[J]. arXiv preprint arXiv:2302.01701, 2023. [Link](https://arxiv.org/abs/2302.01701)
# 2022
## Journal
* Luo G, Zhang H, Yuan Q, et al. ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9954322/)
* Zheng Q, Zhang Y. DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting[J]. IEEE Transactions on Big Data, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9729487/)
* Pu B, Liu J, Kang Y, et al. MVSTT: A Multiview Spatial-Temporal Transformer Network for Traffic-Flow Forecasting[J]. IEEE Transactions on Cybernetics, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9983531/)
* Luo S. RTS-GAT: Spatial Graph Attention-Based Spatio-Temporal Flow Prediction for Big Data Retailing[J]. IEEE Access, 2022, 10: 133232-133243. [Link](https://ieeexplore.ieee.org/abstract/document/9992002/)
* Zuo J, Zeitouni K, Taher Y, et al. Graph convolutional networks for traffic forecasting with missing values[J]. Data Mining and Knowledge Discovery, 2022: 1-35. [Link](https://link.springer.com/article/10.1007/s10618-022-00903-7) [Code](https://github.com/JingweiZuo/GCN-M)
* Zheng F, Zhao J, Ye J, et al. Metro OD Matrix Prediction based on Multi-view Passenger Flow Evolution Trend Modeling[J]. IEEE Transactions on Big Data, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9991082/) [Code](https://github.com/zfrInSIAT/MVPF-code)
* Qiu Z, Zhu T, Jin Y, et al. A Graph Attention Fusion Network for Event-Driven Traffic Speed Prediction[J]. Information Sciences, 2022. [Link](https://www.sciencedirect.com/science/article/pii/S0020025522014918)
* Xue R, Zhao S, Han F. An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9960956/)
* Wang C, Zhang K, Wang H, et al. Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search[J]. ACM Transactions on Knowledge Discovery from Data, 2022. [Link](https://dl.acm.org/doi/abs/10.1145/3571285)
* Lin L, Li W, Zhu L. Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction[J]. Sustainability, 2022, 14(24): 16701. [Link](https://www.mdpi.com/2071-1050/14/24/16701)
* Wang C, Tian R, Hu J, et al. A Trend Graph Attention Network for Traffic Prediction[J]. Information Sciences, 2022. [Link](https://www.sciencedirect.com/science/article/pii/S0020025522015432)
* Guo C, Chen C H, Hwang F J, et al. Fast Spatiotemporal Learning Framework for Traffic Flow Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9966328/)
* Zhu C, Yu C, Huo J. Research on spatio-temporal network prediction model of parallel-series traffic flow based on Transformer and GCAT[J]. Physica A: Statistical Mechanics and its Applications, 2022: 128414. [Link](https://www.sciencedirect.com/science/article/pii/S0378437122009724)
* Li R, Zhang F, Li T, et al. DMGAN: Dynamic Multi-Hop Graph Attention Network for Traffic Forecasting[J]. IEEE Transactions on Knowledge and Data Engineering, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9944937/) [Code](https://github.com/EEHITer/2022-TKDE-DMGAN-Pytorch/tree/main)
* Li M, Tang Y, Ma W. Few-Sample Traffic Prediction With Graph Networks Using Locale as Relational Inductive Biases[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9945664/) [Code](https://github.com/MingxiLii/LocaleGN)
* He Z, Zhao C, Huang Y. Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network[J]. Applied Sciences, 2022, 12(11): 5731. [Link](https://www.mdpi.com/1663940) [Code](https://github.com/yiminghzc/MDST-GNN)
* Zhao Y, Lin Y, Wen H, et al. Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9945663/) [Code](https://github.com/yijizhao/STPGCN)
* Li H, Jin D, Li X J, et al. A Dynamic Heterogeneous Graph Convolution Network For Traffic Flow Prediction[J]. The Computer Journal, 2022. [Link](https://academic.oup.com/comjnl/advance-article-abstract/doi/10.1093/comjnl/bxac156/6832431)
* Xu Y, Cai X, Wang E, et al. Dynamic Traffic Correlations based Spatio-Temporal Graph Convolutional Network for Urban Traffic Prediction[J]. Information Sciences, 2022. [Link](https://www.sciencedirect.com/science/article/pii/S0020025522013779)
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* Luo D, Zhao D, Cao Z, et al. M3AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9940600/)
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## Conference
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