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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 month ago
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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 (over 4 years ago)
Default Branch: master
Last Pushed: 2024-08-07T11:55:53.000Z (5 months ago)
Last Synced: 2024-10-23T10:24:21.272Z (about 2 months ago)
Size: 4.66 MB
Stars: 1,052
Watchers: 36
Forks: 190
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:

![](https://github.com/jwwthu/GNN4Traffic/blob/master/stats/count.png)

Top conferences with paper counts:

![](https://github.com/jwwthu/GNN4Traffic/blob/master/stats/top-conferences.png)

Top journals with paper counts:

![](https://github.com/jwwthu/GNN4Traffic/blob/master/stats/top-journals.png)

# 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)

* Yin X, Zhang W, Zhang S. Spatiotemporal dynamic graph convolutional network for traffic speed forecasting[J]. Information Sciences, 2023: 119056. [Link](https://www.sciencedirect.com/science/article/pii/S0020025523006412)

* 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)

* 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](https://www.mdpi.com/2078-2489/14/4/228)

* 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)

* 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](https://www.sciencedirect.com/science/article/pii/S0957417423007054)

* 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)

* 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](https://www.hindawi.com/journals/jat/2023/3208535/)

* 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](https://www.mdpi.com/2076-3417/13/5/2899)

* 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)

* 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](https://www.sciencedirect.com/science/article/pii/S156849462300193X)

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* Wang Z, Ding D, Liang X. TYRE: A dynamic graph model for traffic prediction[J]. Expert Systems with Applications, 2023, 215: 119311. [Link](https://www.sciencedirect.com/science/article/pii/S0957417422023296) [Code](https://github.com/wzhtxy/Traffic-dYnamic-gRaph-modEl)

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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)

* 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](https://arxiv.org/abs/2305.09703) [Code](https://github.com/gorgen2020/DVGNN)

* 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)

* 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](https://arxiv.org/abs/2304.08640) [Code](https://github.com/baixianghuang/travel)

* 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)

* 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](https://arxiv.org/abs/2302.12973)

* 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](https://arxiv.org/abs/2302.11774)

* Luo X, Zhu C, Zhang D, et al. Dynamic Graph Convolution Network with Spatio-Temporal Attention Fusion for Traffic Flow Prediction[J]. arXiv preprint arXiv:2302.12598, 2023. [Link](https://arxiv.org/abs/2302.12598)

* 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)

* Jin G, Liang Y, Fang Y, et al. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey[J]. arXiv preprint arXiv:2303.14483, 2023. [Link](https://arxiv.org/abs/2303.14483)

* Huang Y, Song X, Zhu Y, et al. Traffic Prediction with Transfer Learning: A Mutual Information-based Approach[J]. arXiv preprint arXiv:2303.07184, 2023. [Link](https://arxiv.org/abs/2303.07184)

* Prabowo A, Shao W, Xue H, et al. Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting[J]. arXiv preprint arXiv:2302.09956, 2023. [Link](https://arxiv.org/abs/2302.09956) [Code](https://github.com/aprbw/G-SWaN)

* 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)

* Cai B, Wang Y, Huang C, et al. GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow[J]. Sensors, 2022, 22(22): 8880. [Link](https://www.mdpi.com/1424-8220/22/22/8880)

* Liang D, Ruobin G, Suganthan P N, et al. Graph ensemble deep random vector functional link network for traffic forecasting[J]. Applied Soft Computing, 2022: 109809. [Link](https://www.sciencedirect.com/science/article/pii/S1568494622008584)

* Zou X, Zhang S, Zhang C, et al. Long-term origin-destination demand prediction with graph deep learning[J]. IEEE Transactions on Big Data, 2022. [Link](https://ieeexplore.ieee.org/abstract/document/9369004/)

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## Conference

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* 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](https://ieeexplore.ieee.org/abstract/document/10022299/)

* 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](https://link.springer.com/chapter/10.1007/978-981-19-9297-1_7)

* 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](https://ieeexplore.ieee.org/abstract/document/10020288/) [Code](https://github.com/dophanh-26/3D-STGPCN)

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* 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](https://ieeexplore.ieee.org/abstract/document/10020347/)

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* 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](https://arxiv.org/abs/2110.10380) [Code](https://github.com/HyunWookL/PM-MemNet)

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* 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](https://ieeexplore.ieee.org/abstract/document/9922211/)

* 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](https://ieeexplore.ieee.org/abstract/document/9921809/)

* 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](https://ieeexplore.ieee.org/abstract/document/9892016/)

* 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](https://ieeexplore.ieee.org/abstract/document/9919973/)

* 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](https://ieeexplore.ieee.org/abstract/document/9921900/)

* 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](https://ieeexplore.ieee.org/abstract/document/9922259/)

* 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](https://ieeexplore.ieee.org/abstract/document/9892885/)

* 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](https://dl.acm.org/doi/abs/10.1145/3511808.3557540)

* 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](https://dl.acm.org/doi/abs/10.1145/3511808.3557243)

* 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](https://ieeexplore.ieee.org/abstract/document/9912866/)

* 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](https://www.researchgate.net/profile/Jichao-Bi/publication/362830720_MGC-GAN_Multi-Graph_Convolutional_Generative_Adversarial_Networks_for_Accurate_Citywide_Traffic_Flow_Prediction/links/6301d129aa4b1206fac7aa20/MGC-GAN-Multi-Graph-Convolutional-Generative-Adversarial-Networks-for-Accurate-Citywide-Traffic-Flow-Prediction.pdf)

* 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](https://link.springer.com/chapter/10.1007/978-3-031-15931-2_50)

* 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](https://dl.acm.org/doi/abs/10.1145/3511808.3557705)

* Kim D, Cho Y, Kim D, et al. Residual Correction in Real-Time Traffic Forecasting[C]. CIKM, 2022. [Link](https://dl.acm.org/doi/abs/10.1145/3511808.3557432)

* 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](https://ieeexplore.ieee.org/document/9835338/)

* 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](https://link.springer.com/chapter/10.1007/978-3-031-05933-9_32)

* 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](https://ieeexplore.ieee.org/abstract/document/9746497/)

* 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](https://link.springer.com/chapter/10.1007/978-3-031-00129-1_26)

* 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](https://dl.acm.org/doi/abs/10.14778/3551793.3551827) [Code](https://github.com/zezhishao/D2STGNN)

* 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](https://proceedings.mlr.press/v162/lan22a.html) [Code](https://github.com/SYLan2019/DSTAGNN)

* Wang Z, Jiang R, Xue H, et al. Event-Aware Multimodal Mobility Nowcasting[C]. AAAI, 2022. [Link](https://ojs.aaai.org/index.php/AAAI/article/view/20342) [Code](https://github.com/underdoc-wang/EAST-Net)

* 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](https://www.ijcai.org/proceedings/2022/545) [Code](https://github.com/kevin-xuan/FOGS)

* 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](https://ojs.aaai.org/index.php/AAAI/article/view/20587) [Code](https://github.com/jeongwhanchoi/STG-NCDE)

* 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](https://ieeexplore.ieee.org/abstract/document/9771883/)

* 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](https://dl.acm.org/doi/abs/10.1145/3534678.3539397) [Code](https://github.com/dcliu99/MSDR)

* 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](https://link.springer.com/chapter/10.1007/978-3-031-00123-9_15)

* 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](https://ieeexplore.ieee.org/abstract/document/9747016/)

* Semenova N, Porvatov V, Tishin V, et al. Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation[C]. PKDD, 2022. [Link](https://arxiv.org/abs/2207.05835) [Code](https://github.com/Vloods/TransTTE_demo)

* 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](https://www.ijcai.org/proceedings/2022/309)

* 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](https://dl.acm.org/doi/abs/10.1145/3534678.3539422)

* 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](https://dl.acm.org/doi/abs/10.1145/3534678.3539396) [Code](https://github.com/zezhishao/STEP)

* 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](https://dl.acm.org/doi/abs/10.1145/3485447.3511990)

* 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](https://www.ijcai.org/proceedings/2022/328) [Code](https://github.com/alipay/RGSL.git)

* Lu B, Gan X, Zhang W, et al. Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer[C]. KDD, 2022. [Link](https://arxiv.org/abs/2205.13947) [Code](https://github.com/RobinLu1209/ST-GFSL)

* Tang J, Qian T, Liu S, et al. Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting[C]. IJCNN, 2022. [Link](https://arxiv.org/abs/2202.12586)

* Ji J, Wang J, Jiang Z, et al. STDEN: Towards Physics-guided Neural Networks for Traffic Flow Prediction[J]. AAAI, 2022. [Link](https://ojs.aaai.org/index.php/AAAI/article/view/20322) [Code](https://github.com/Echo-Ji/STDEN)

* 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](https://openreview.net/forum?id=wv6g8fWLX2q) [Code](https://github.com/tamps2gcnets/TAMP_S2GCNets.git)

* 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](https://arxiv.org/abs/2208.06947)

* 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](https://ieeexplore.ieee.org/abstract/document/9776256/)

* 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](https://dl.acm.org/doi/abs/10.1145/3534678.3539093)

## 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](https://arxiv.org/abs/2210.07765) [Code](https://github.com/YihongT/HGARN)

* 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](https://arxiv.org/abs/2211.00641) [Code](https://github.com/Daftstone/Traffic4cast)

* Tang Y, He J, Zhao Z. HGARN: Hierarchical Graph Attention Recurrent Network for Human Mobility Prediction[J]. arXiv preprint arXiv:2210.07765, 2022. [Link](https://arxiv.org/abs/2210.07765) [Code](https://github.com/YihongT/HGARN)

* 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](https://arxiv.org/abs/2203.10961)

* 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](https://arxiv.org/abs/2211.08903)

* 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](https://arxiv.org/abs/2211.03033)

* 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](https://arxiv.org/abs/2211.08541)

* 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](https://arxiv.org/abs/2211.04988)

* 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](https://arxiv.org/abs/2210.16193)

* Cini A, Marisca I, Bianchi F M, et al. Scalable Spatiotemporal Graph Neural Networks[J]. arXiv preprint arXiv:2209.06520, 2022. [Link](https://arxiv.org/abs/2209.06520)

* 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](https://arxiv.org/abs/2210.16789)

* Shin Y, Yoon Y. PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting[J]. arXiv preprint arXiv:2202.08982, 2022. [Link](https://arxiv.org/abs/2202.08982) [Code](https://github.com/yuyolshin/PGCN)

* Pu Y. Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction[J]. arXiv preprint arXiv:2210.00704, 2022. [Link](https://arxiv.org/abs/2210.00704) [Code](https://github.com/Dandelionym/CDVGM)

* 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](https://arxiv.org/abs/2210.02737) [Code](https://github.com/ZLBryant/STGCGRN)

* 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](https://arxiv.org/abs/2210.01799)

* 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](https://arxiv.org/abs/2209.03858)

* 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](https://arxiv.org/abs/2205.04762)

* Feng A, Tassiulas L. Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2207.05064, 2022. [Link](https://arxiv.org/abs/2207.05064v1)

* 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](https://arxiv.org/abs/2203.00489)

* 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](https://arxiv.org/abs/2207.10830) [Code](https://github.com/jinguangyin/Auto-DSTSGN)

* 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](https://arxiv.org/abs/2203.10961)

* 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](https://arxiv.org/abs/2206.15005) [Code](https://github.com/liangzhehan/CMOD)

* 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](https://arxiv.org/abs/2204.01618)

* 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](https://arxiv.org/abs/2208.03063v1) [Code](https://github.com/juyongjiang/DAAGCN)

* 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](https://arxiv.org/abs/2205.14593) [Code](https://github.com/Rising0321/HMOD)

* 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](https://arxiv.org/abs/2203.03965) [Code](https://github.com/MingxiLii/LocaleGN)

* 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](https://arxiv.org/abs/2202.06727)

* 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](https://arxiv.org/abs/2201.09686) [Code](https://github.com/niuffs/LSCGF)

* 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](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4142192) [Code](https://github.com/SYLan2019/MH-ASTIGCN)

* 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](https://arxiv.org/abs/2202.08408)

* Satorras V G, Rangapuram S S, Januschowski T. Multivariate Time Series Forecasting with Latent Graph Inference[J]. arXiv preprint arXiv:2203.03423, 2022. [Link](https://arxiv.org/abs/2203.03423)

* 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](https://arxiv.org/abs/2207.01301)

* 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](https://arxiv.org/abs/2208.08218)

* Rodrigues F. On the importance of stationarity, strong baselines and benchmarks in transport prediction problems[J]. arXiv preprint arXiv:2203.02954, 2022. [Link](https://arxiv.org/abs/2203.02954)

* 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](https://arxiv.org/abs/2206.05602)

* 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](https://arxiv.org/abs/2205.01480) [Code](https://github.com/zhangshqii/RGCRN)

* 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](https://arxiv.org/abs/2206.03128)

* Liu A, Zhang Y. Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting[J]. arXiv preprint arXiv:2205.08689, 2022. [Link](https://arxiv.org/abs/2205.08689)

* 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](https://arxiv.org/abs/2204.02650)

* 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](https://arxiv.org/abs/2112.02740)

* 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](https://arxiv.org/abs/2203.10749) [Code](https://github.com/zhangshqii/STCGAT)

* Tygesen M N, Pereira F C, Rodrigues F. Unboxing the graph: Neural Relational Inference for Mobility Prediction[J]. arXiv preprint arXiv:2201.10307, 2022. [Link](https://arxiv.org/abs/2201.10307)

# 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](https://dl.acm.org/doi/abs/10.1145/3451394) [Code](https://github.com/FIBLAB/3D-DGCN)

* 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](https://www.worldscientific.com/doi/abs/10.1142/S0129183121501588)

* 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](https://www.sciencedirect.com/science/article/pii/S0968090X21003740)

* 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](https://www.sciencedirect.com/science/article/pii/S0952197621003390)

* 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](https://www.hindawi.com/journals/wcmc/2021/1997212/)

* 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](https://ieeexplore.ieee.org/abstract/document/9381554)

* 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](https://ieeexplore.ieee.org/abstract/document/9406409/) [data](https://github.com/skzhangPKU/GTA)

* 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](https://www.mdpi.com/1059488)

* 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](https://link.springer.com/article/10.1007/s11276-021-02672-5)

* 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](https://ieeexplore.ieee.org/abstract/document/9376697/)

* 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](https://arxiv.org/abs/2203.12505)

* 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](https://www.worldscientific.com/doi/abs/10.1142/S0218126622501122)

* 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](https://www.mdpi.com/1274862)

* 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](https://www.worldscientific.com/doi/abs/10.1142/S0218001421590436)

* 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](https://link.springer.com/article/10.1007/s10489-021-02648-0)

* 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](https://ieeexplore.ieee.org/abstract/document/9506840/)

* 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](https://www.mdpi.com/1424-8220/21/20/6735) [Data](https://github.com/hzqhappy/SPTMN)

* 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](https://ieeexplore.ieee.org/abstract/document/9511315/)

* Zhu J, Tao C, Deng H, et al. AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting[J]. IEEE Access. [Link](https://ieeexplore.ieee.org/document/9363197) [Code](https://github.com/lehaifeng/T-GCN/tree/master/AST-GCN)

* Buroni G, Lebichot B, Bontempi G. AST-MTL: An Attention-based Multi-Task Learning Strategy for Traffic Forecasting[J]. IEEE Access, 2021. [Link](https://ieeexplore.ieee.org/abstract/document/9439877/) [Code](https://github.com/giobbu/AST-MTL)

* 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](https://www.sciencedirect.com/science/article/pii/S2352864821000675)

* 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](https://www.mdpi.com/1424-8220/21/24/8468)

* Jiang H, Li L, Xian H, et al. Crowd Flow Prediction for Social Internet-of-Things Systems Based on the Mobile Network Big Data[J]. IEEE Transactions on Computational Social Systems, 2021. [Link](https://ieeexplore.ieee.org/abstract/document/9378810/)

* Pan C, Zhu J, Kong Z, et al. DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting[J]. Electronics, 2021, 10(9): 1014. [Link](https://www.mdpi.com/1085378)

* Bai L, Yao L, Wang X, et al. Deep spatial-temporal sequence modeling for multi-step passenger demand prediction[J]. Future Generation Computer Systems, 2021. [Link](https://www.sciencedirect.com/science/article/pii/S0167739X21000832)

* Lv Z, Li J, Dong C, et al. DeepSTF: A Deep Spatial–Temporal Forecast Model of Taxi Flow[J]. The Computer Journal, 2021. [Link](https://academic.oup.com/comjnl/advance-article-abstract/doi/10.1093/comjnl/bxab178/6428744) [Code](https://github.com/qdu318/DeepSTF.git)

* 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](https://link.springer.com/article/10.1007/s00521-021-05966-z)

* 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](https://link.springer.com/article/10.1007/s00521-021-06092-6)

* 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](https://ieeexplore.ieee.org/abstract/document/9657493/)

* 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](https://www.sciencedirect.com/science/article/pii/S0020025521006976)

* 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](https://ieeexplore.ieee.org/abstract/document/9462708/)

* 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](https://journals.sagepub.com/doi/abs/10.1177/03611981211012003)

* 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](https://ieeexplore.ieee.org/abstract/document/9665313/)

* 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](https://ieeexplore.ieee.org/abstract/document/9340313)

* 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](https://link.springer.com/article/10.1007/s12204-021-2382-5)

* 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](https://www.sciencedirect.com/science/article/pii/S0925231221003477)

* Fang M, Tang L, Yang X, et al. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021. [Link](https://ieeexplore.ieee.org/abstract/document/9329073)

* 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](https://dl.acm.org/doi/abs/10.1145/3451356)

* Liu D, Xu X, Xu W, et al. Graph convolutional network: traffic speed prediction fused with traffic flow data[J]. Sensors, 2021, 21(19): 6402. [Link](https://www.mdpi.com/1285612)

* Hu N, Zhang D, Xie K, et al. Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting[J]. Connection Science, 2021: 1-20. [Link](https://www.tandfonline.com/doi/abs/10.1080/09540091.2021.2006607)

* Zhang Q, Yu K, Guo Z, et al. Graph Neural Networks-driven Traffic Forecasting for Connected Internet of Vehicles[J]. IEEE Transactions on Network Science and Engineering, 2021. [Link](https://ieeexplore.ieee.org/document/9618828)

* Wang Q, Xu C, Zhang W, et al. GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs[J]. IEEE Signal Processing Letters, 2021. [Link](https://ieeexplore.ieee.org/abstract/document/9314202)

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## 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](https://arxiv.org/abs/2104.10644)

* 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](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.58)

* Li B, Guo T, Wang Y, et al. Adaptive Graph Co-Attention Networks for Traffic Forecasting[C]//PAKDD (1). 2021: 263-276. [Link](https://link.springer.com/chapter/10.1007/978-3-030-75762-5_22)

* 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](https://arxiv.org/abs/2111.00724)

* 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](https://ieeexplore.ieee.org/abstract/document/9679000/)

* 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](https://ieeexplore.ieee.org/abstract/document/9550673/)

* 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](https://arxiv.org/abs/2105.05504)

* 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](https://ieeexplore.ieee.org/abstract/document/9564626/)

* 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](https://link.springer.com/chapter/10.1007/978-981-16-6324-6_44)

* 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](https://ieeexplore.ieee.org/abstract/document/9636559/)

* 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](https://ojs.aaai.org/index.php/AAAI/article/view/16107)

* 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](https://ieeexplore.ieee.org/abstract/document/9671705/)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-86514-6_20) [Code](https://github.com/deepkashiwa20/ODCRN.git)

* 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](https://arxiv.org/abs/2012.08080) [Code](https://github.com/Essaim/CGCDemandPrediction)

* 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](https://ieeexplore.ieee.org/abstract/document/9671682/) [Code](https://github.com/LMZZML/CoGNN/)

* 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](https://arxiv.org/abs/2106.05223)

* 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](https://dl.acm.org/doi/abs/10.1145/3474717.3483920)

* Shang C, Chen J, Bi J. Discrete Graph Structure Learning for Forecasting Multiple Time Series[C]. International Conference on Learning Representations (ICLR), 2021. [Link](https://openreview.net/forum?id=WEHSlH5mOk) [Code](https://github.com/chaoshangcs/GTS)

* 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](https://ieeexplore.ieee.org/abstract/document/9564998/)

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* 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](https://link.springer.com/chapter/10.1007/978-3-030-82153-1_22)

* 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](https://ieeexplore.ieee.org/abstract/document/9458855/)

* Derrow-Pinion A, She J, Wong D, et al. ETA Prediction with Graph Neural Networks in Google Maps[C]. CIKM, 2021. [Link](https://arxiv.org/abs/2108.11482)

* 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](https://ieeexplore.ieee.org/abstract/document/9649044/)

* 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](https://arxiv.org/abs/2007.15531v1) [Code](https://github.com/boreshkinai/fc-gaga)

* 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](https://ieeexplore.ieee.org/abstract/document/9458623/)

* 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](https://ieeexplore.ieee.org/abstract/document/9458919/)

* 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](https://arxiv.org/abs/2105.04991)

* 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](https://ieeexplore.ieee.org/abstract/document/9674238/)

* 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](https://engineering.purdue.edu/~lusu/papers/ICDCS2021.pdf)

* 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](https://github.com/guokan987/HGCN/blob/main/paper/3399.GuoK.pdf) [Code](https://github.com/guokan987/HGCN)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-86486-6_20)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-72792-5_53)

* 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](https://ieeexplore.ieee.org/abstract/document/9534063/) [Code](https://github.com/zqylen/MAGCN)

* 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](https://ieeexplore.ieee.org/abstract/document/9546907/)

* 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](https://ieeexplore.ieee.org/abstract/document/9564544/)

* 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](https://ieeexplore.ieee.org/abstract/document/9685639/)

* 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](https://epubs.siam.org/doi/abs/10.1137/1.9781611976700.57)

* 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](https://ieeexplore.ieee.org/abstract/document/9534054/)

* 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](https://ieeexplore.ieee.org/abstract/document/9671619/)

* Jing B, Tong H, Zhu Y. Network of tensor time series[C]//Proceedings of the Web Conference 2021. 2021: 2425-2437. [Link](https://dl.acm.org/doi/abs/10.1145/3442381.3449969)

* 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](https://dl.acm.org/doi/abs/10.1145/3442381.3449928)

* 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](https://arxiv.org/abs/2106.06064) [Code](https://github.com/networkslab/rnn_flow)

* 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](https://ieeexplore.ieee.org/abstract/document/9327103/)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-92270-2_12)

* 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](https://arxiv.org/abs/2012.09641) [Code](https://github.com/MengzhangLI/STFGNN)

* 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](https://arxiv.org/abs/2106.12931) [Code](https://github.com/square-coder/STGODE)

* 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](https://ieeexplore.ieee.org/abstract/document/9685187/)

* 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](https://ieeexplore.ieee.org/abstract/document/9440573/)

* 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](https://arxiv.org/abs/2104.00055)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-86365-4_21)

* 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](https://ieeexplore.ieee.org/abstract/document/9625430/)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-85462-1_7)

* 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](https://ieeexplore.ieee.org/abstract/document/9540169/)

* 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](https://ieeexplore.ieee.org/abstract/document/9679077/)

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

* 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](https://ojs.aaai.org/index.php/AAAI/article/view/17761) [Code](https://github.com/jillbetty001/ST-GDN)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-86472-9_29) [Code](https://github.com/css518/HSTGNN)

* 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](https://ojs.aaai.org/index.php/AAAI/article/view/16104) [Code](https://github.com/mingqian000/TrGNN)

* 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](https://ieeexplore.ieee.org/abstract/document/9674490/)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-87571-8_13)

* 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](https://ieeexplore.ieee.org/abstract/document/9414596/)

* 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](https://arxiv.org/abs/2106.06273) [Code](https://github.com/AprLie/TrafficStream)

* 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](https://dl.acm.org/doi/abs/10.1145/3447548.3467236)

* 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](https://ieeexplore.ieee.org/abstract/document/9564890/)

* 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](https://ieeexplore.ieee.org/abstract/document/9551442/)

* 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](https://arxiv.org/abs/2104.12518) [Code](https://github.com/AmitRoy7781/USTGCN)

* 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](https://arxiv.org/abs/2105.04100) [Code](https://github.com/Z-GCNETs/Z-GCNETs.git)

## 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](https://link.springer.com/chapter/10.1007/978-981-16-2609-8_9)

## Preprint

* Fu J, Zhou W, Chen Z. Bayesian Graph Convolutional Network for Traffic Prediction[J]. arXiv preprint arXiv:2104.00488, 2021. [Link](https://arxiv.org/abs/2104.00488)

* 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](https://arxiv.org/abs/2112.02736)

* 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](https://arxiv.org/abs/2101.01000v1)

* 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](https://arxiv.org/abs/2109.09506)

* 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](https://arxiv.org/abs/2112.02264)

* 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](https://arxiv.org/abs/2104.14917) [Code](https://github.com/tsinghua-fib-lab/Traffic-Benchmark)

* 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](https://arxiv.org/abs/2101.07425)

* 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](https://arxiv.org/abs/2104.05914)

* 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](https://arxiv.org/abs/2107.01528)

* Grigsby J, Wang Z, Qi Y. Long-Range Transformers for Dynamic Spatiotemporal Forecasting[J]. arXiv preprint arXiv:2109.12218, 2021. [Link](https://arxiv.org/abs/2109.12218)

* 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](https://arxiv.org/abs/2108.03594)

* 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](https://arxiv.org/abs/2107.13226)

* 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](https://arxiv.org/abs/2108.03900) [Code](https://github.com/start2020/Multi-View_TRGRU)

* 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](https://arxiv.org/abs/2111.11337)

* 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](https://arxiv.org/abs/2107.00894)

* Gao F, Wang Z, Liu Z. Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction[J]. arXiv preprint arXiv:2109.00924, 2021. [Link](https://arxiv.org/abs/2109.00924)

* 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](https://arxiv.org/abs/2101.00752)

* 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](https://arxiv.org/abs/2108.02424)

* 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](https://arxiv.org/abs/2105.13591)

* 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](https://arxiv.org/abs/2103.06126)

* Huang C. STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Prediction[J]. arXiv preprint arXiv:2107.04980, 2021. [Link](https://arxiv.org/abs/2107.04980)

* 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](https://arxiv.org/abs/2110.01535)

# 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](https://ieeexplore.ieee.org/abstract/document/9173702/) [Code](https://github.com/sam101340/GAGCN-BC-20200720)

* 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](https://www.sciencedirect.com/science/article/pii/S0968090X19309349)

* 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](https://onlinelibrary.wiley.com/doi/10.1111/tgis.12641)

* 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](https://ieeexplore.ieee.org/abstract/document/9216590/)

* 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](https://www.tandfonline.com/doi/abs/10.1080/13658816.2019.1697879)

* 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](https://www.hindawi.com/journals/jat/2020/7586154/)

* 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](https://www.sciencedirect.com/science/article/abs/pii/S1570870520301803)

* 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](https://ieeexplore.ieee.org/abstract/document/9055022/)

* 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](https://aip.scitation.org/doi/abs/10.1063/5.0007174)

* James J Q. Citywide traffic speed prediction: A geometric deep learning approach[J]. Knowledge-Based Systems, 2020: 106592. [Link](https://www.sciencedirect.com/science/article/pii/S0950705120307218)

* 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](https://www.sciencedirect.com/science/article/pii/S0968090X20307774)

* 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](https://dl.acm.org/doi/abs/10.1145/3381005)

* 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](https://ieeexplore.ieee.org/abstract/document/9136910/) [Code](https://github.com/JinleiZhangBJTU/ResNet-LSTM-GCN)

* Yu L, Du B, Hu X, et al. Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction[J]. Neurocomputing, 2020. [Link](https://www.sciencedirect.com/science/article/pii/S092523122031451X) [Code](https://github.com/yule-BUAA/DSTGCN)

* 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](https://link.springer.com/article/10.1007/s11042-020-08803-y)

* 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](https://ieeexplore.ieee.org/abstract/document/9261496)

* 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](https://ieeexplore.ieee.org/abstract/document/9190068/) [Code](https://github.com/guokan987/DGCN)

* 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](https://journals.sagepub.com/doi/abs/10.1177/0361198120919399) [Code](https://github.com/alzmxx/OD_Prediction)

* 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](https://ieeexplore.ieee.org/document/9207934)

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* 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](https://www.sciencedirect.com/science/article/pii/S0968090X20306756)

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* 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](https://ieeexplore.ieee.org/document/9239873)

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* 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](https://ieeexplore.ieee.org/abstract/document/9063494/)

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* 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](https://ieeexplore.ieee.org/abstract/document/9247476/) [Code](https://github.com/Stanislas0/ToGCN-V2X)

* 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](https://ieeexplore.ieee.org/abstract/document/8968739/)

* 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](http://itiis.org/digital-library/23851)

* Li W, Wang X, Zhang Y, et al. Traffic Flow Prediction over Muti-Sensor Data Correlation with Graph Convolution Network[J]. Neurocomputing, 2020. [Link](https://www.sciencedirect.com/science/article/pii/S0925231220318191)

* 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](https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12644)

* 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](https://ieeexplore.ieee.org/abstract/document/9261122) [Code](https://github.com/YibinShen/TTPNet)

* Wang Y, Fang S, Zhang C, et al. TVGCN: Time-Variant Graph Convolutional Network for Traffic Forecasting[J]. Neurocomputing, 2021. [Link](https://www.sciencedirect.com/science/article/pii/S0925231221016805)

* 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](https://www.sciencedirect.com/science/article/pii/S0968090X20305805)

* Zhang Y, Lu M, Li H. Urban Traffic Flow Forecast Based on FastGCRNN[J]. Journal of Advanced Transportation, 2020, 2020. [Link](https://www.hindawi.com/journals/jat/2020/8859538/)

* 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](https://ieeexplore.ieee.org/abstract/document/9140389/)

## 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](https://ieeexplore.ieee.org/abstract/document/9288187/)

* 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](https://ieeexplore.ieee.org/abstract/document/9158447/)

* 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](https://arxiv.org/abs/2007.02842) [Code](https://github.com/LeiBAI/AGCRN)

* 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](https://arxiv.org/abs/2101.12465) [Code](https://github.com/l852888/AGSTN)

* 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](https://ieeexplore.ieee.org/abstract/document/9294470/)

* 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](https://ieeexplore.ieee.org/abstract/document/9216966/)

* 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](https://dl.acm.org/doi/10.1145/3394486.3403118) [Code](https://github.com/nnzhan/MTGNN)

* 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](https://dl.acm.org/doi/10.1145/3394486.3403320)

* 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](https://arxiv.org/abs/2004.10958)

* 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](https://ieeexplore.ieee.org/document/9207457)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3411873) 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](https://arxiv.org/abs/1912.01242)

* 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](https://dl.acm.org/doi/abs/10.1145/3366423.3380101)

* 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](https://ieeexplore.ieee.org/abstract/document/9294627/) [Code](https://github.com/RomainLITUD/DGCN_traffic_forecasting)

* 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](https://ieeexplore.ieee.org/abstract/document/9181234/)

* 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](https://ieeexplore.ieee.org/abstract/document/9359215/)

* 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](https://dl.acm.org/doi/abs/10.1145/3409501.3409539)

* 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](https://arxiv.org/abs/1909.04019)

* 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](https://ieeexplore.ieee.org/document/9534064)

* 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](https://project.inria.fr/aaltd20/files/2020/08/AALTD_20_paper_Sanchez.pdf) [Code (empty till 2022/03/01)](https://github.com/csalort/GANNSTER)

* 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](https://ieeexplore.ieee.org/abstract/document/9294700/)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-55393-7_39)

* 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](https://ojs.aaai.org//index.php/AAAI/article/view/5477) [Code](https://github.com/zhengchuanpan/GMAN)

* 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](https://ieeexplore.ieee.org/abstract/document/9294580/) [Code (Still empty till 2022/03/01)](https://github.com/simmonssong/GAC-Net)

* 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](https://dl.acm.org/doi/abs/10.1145/3397536.3422257) [Code](https://github.com/Fanglanc/DKFN)

* 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](https://ieeexplore.ieee.org/abstract/document/9378450/)

* 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](https://ieeexplore.ieee.org/abstract/document/9282828/)

* 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](https://dl.acm.org/doi/10.1145/3394486.3403294) [Code](https://github.com/didi/heteta)

* 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](https://dl.acm.org/doi/10.1145/3394486.3403358)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3417411)

* 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](http://urban-computing.com/urbcomp2021/file/UrbComp2021_Full_Xi.pdf)

* 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](https://arxiv.org/abs/2004.11924) [Code](https://github.com/FelixOpolka/Mobility-Flows-Neural-Networks)

* 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](https://www.ijcai.org/Proceedings/2020/326)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-62005-9_30)

* 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](https://aaai.org/ojs/index.php/AAAI/article/view/5758)

* 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](https://ieeexplore.ieee.org/abstract/document/9207049/) [Code](https://github.com/start2020/Multi-STGCnet)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3412054)

* 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](https://dl.acm.org/doi/abs/10.1145/3395260.3395266)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-59410-7_30)

* 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](https://dl.acm.org/doi/abs/10.1145/3474717.3483921)

* 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](https://ieeexplore.ieee.org/abstract/document/9101359)

* 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](https://ieeexplore.ieee.org/abstract/document/9101647) [Code](https://github.com/hujilin1229/od-pred)

* 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](https://ieeexplore.ieee.org/abstract/document/9294742/)

* 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](https://openreview.net/forum?id=B1lGU64tDr) [Code](https://github.com/fanyang01/relational-ssm)

* 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](https://ieeexplore.ieee.org/abstract/document/9162310/)

* 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](https://ojs.aaai.org//index.php/AAAI/article/view/5480) [Code](https://github.com/zzyy0929/AAAI2020-RiskOracle/)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-59410-7_49)

* 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](https://ieeexplore.ieee.org/abstract/document/9207433/)

* 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](https://www.aaai.org/ojs/index.php/AAAI/article/view/5471) [Code](https://github.com/Vvrep/SHARE-parking_availability_prediction-Pytorch)

* Li A, Axhausen K W. Short-term Traffic Demand Prediction using Graph Convolutional Neural Networks[C]. AGILE: GIScience Series, 2020, 1: 1-14. [Link](https://agile-giss.copernicus.org/articles/1/12/2020/agile-giss-1-12-2020.html)

* 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](https://ascelibrary.org/doi/abs/10.1061/9780784483152.014)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-67670-4_27)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3411894) [Code](https://github.com/RobinLu1209/STAG-GCN)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3411941) [Code](https://github.com/shurexiyue/ST-CGA)

* 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](https://ieeexplore.ieee.org/abstract/document/9263680)

* 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](https://iopscience.iop.org/article/10.1088/1755-1315/587/1/012065/meta)

* 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](https://dl.acm.org/doi/abs/10.1145/3474717.3484207)

* 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](https://ojs.aaai.org//index.php/AAAI/article/view/5438) [Author's Code](https://github.com/wanhuaiyu/STSGCN) [Code1](https://github.com/Davidham3/STSGCN) [Code2](https://github.com/mcdragon/STSGCN)

* 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](https://ojs.aaai.org/index.php/AAAI/article/view/5470)

* 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](https://proceedings.neurips.cc/paper/2020/hash/cdf6581cb7aca4b7e19ef136c6e601a5-Abstract.html) [Code](https://github.com/microsoft/StemGNN)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3411874)

* 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](https://dl.acm.org/doi/abs/10.1145/3340531.3411940) Note: previously known as ST-GRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting[C] [Link_arxiv](https://arxiv.org/abs/1911.13181)

* 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](http://ebooks.iospress.nl/volumearticle/55105) [Code (Still empty on 2022/03/01)](https://github.com/fsgdrq/STMFM)

* 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](https://ieeexplore.ieee.org/abstract/document/9288309/) [Code](https://github.com/Kelang-Tian/ST-MGAT)

* 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](https://arxiv.org/abs/1912.05693)

* 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](https://dl.acm.org/doi/abs/10.1145/3426826.3426831)

* 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](https://dl.acm.org/doi/10.1145/3366423.3380097)

* 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](https://ieeexplore.ieee.org/abstract/document/9299778/)

* 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](https://ieeexplore.ieee.org/abstract/document/9201971)

* 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](https://dl.acm.org/doi/abs/10.1145/3366423.3380186)

* 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](https://ieeexplore.ieee.org/abstract/document/9149233/)

* 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](https://arxiv.org/abs/2004.08038) [Code](https://github.com/tanwimallick/TL-DCRNN)

* 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](https://arxiv.org/abs/2011.14638)

* 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](https://ieeexplore.ieee.org/abstract/document/9276823/)

## 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](https://arxiv.org/abs/2009.12469)

* 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](https://arxiv.org/abs/2012.13479) [Code](https://github.com/victorchan314/arterial_traffic_flow_predictor)

* 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](https://arxiv.org/abs/2006.11583v1) [Code](https://github.com/lehaifeng/T-GCN/tree/master/A3T)

* 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](https://arxiv.org/abs/2010.07474) [Code](https://github.com/zkx741481546/Auto-STGCN)

* Fu J, Zhou W, Chen Z. Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting[J]. arXiv preprint arXiv:2010.07498, 2020. [Link](https://arxiv.org/abs/2010.07498)

* 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](https://arxiv.org/abs/2007.15189v2)

* Jia C, Wu B, Zhang X P. Dynamic Spatiotemporal Graph Neural Network with Tensor Network[J]. arXiv preprint arXiv:2003.08729, 2020. [Link](https://arxiv.org/abs/2003.08729)

* 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](https://arxiv.org/abs/1908.05365v2) [Code](https://github.com/florishermsen/L-GCN)

* 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](https://arxiv.org/abs/2009.09379)

* 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](https://arxiv.org/abs/2008.03970v1)

* 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](https://arxiv.org/abs/2011.14992)

* 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](https://arxiv.org/abs/2111.01355)

* 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](https://arxiv.org/abs/2009.12157)

* Pian W, Wu Y. Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction[J]. arXiv preprint arXiv:2006.05905, 2020. [Link](https://arxiv.org/abs/2006.05905)

* Xu M, Dai W, Liu C, et al. Spatial-Temporal Transformer Networks for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2001.02908, 2020. [Link](https://arxiv.org/abs/2001.02908)

* Maas T, Bloem P. Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction[J]. arXiv preprint arXiv:2012.05207, 2020. [Link](https://arxiv.org/abs/2012.05207)

# 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](https://www.sciencedirect.com/science/article/pii/S0968090X18313780)

* 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](https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12450)

* Wei L, Yu Z, Jin Z, et al. Dual Graph for Traffic Forecasting[J]. IEEE Access, 2019. [Link](https://ieeexplore.ieee.org/abstract/document/8928590/)

* 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](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220782)

* 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](https://www.mdpi.com/2220-9964/8/9/414)

* 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](https://www.worldscientific.com/doi/abs/10.1142/S0218194019400187)

* 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](https://www.sciencedirect.com/science/article/pii/S0968090X18315389)

* 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](https://www.mdpi.com/2220-9964/8/6/243)

* 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](https://ieeexplore.ieee.org/abstract/document/8697151/)

* 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](https://aip.scitation.org/doi/abs/10.1063/1.5117180%40cha.2020.MACL2020.issue-1)

* Xie Z, Lv W, Huang S, et al. Sequential graph neural network for urban road traffic speed prediction[J]. IEEE Access, 2019. [Link](https://ieeexplore.ieee.org/abstract/document/8708297/)

* 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](https://ieeexplore.ieee.org/abstract/document/8903252/)

* 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](https://ieeexplore.ieee.org/abstract/document/8809901/) [Code](https://github.com/lehaifeng/T-GCN)

* 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](https://ieeexplore.ieee.org/abstract/document/8917706/) [Code](https://github.com/zhiyongc/Graph_Convolutional_LSTM)

## 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](https://ieeexplore.ieee.org/abstract/document/8916778/)

* 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](https://ieeexplore.ieee.org/abstract/document/8997248/)

* 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](https://www.aaai.org/ojs/index.php/AAAI/article/view/3881) [Code-gluon](https://github.com/wanhuaiyu/ASTGCN) [Code-pytorch](https://github.com/wanhuaiyu/ASTGCN-r-pytorch) [Code1](https://github.com/guoshnBJTU/ASTGCN-r-pytorch)

* 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](https://ieeexplore.ieee.org/abstract/document/9060125/)

* 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](https://www.aaai.org/ojs/index.php/AAAI/article/view/3877)

* 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](https://www.aaai.org/ojs/index.php/AAAI/article/view/3821)

* 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](https://ieeexplore.ieee.org/abstract/document/8852211/)

* 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](https://milets19.github.io/papers/milets19_paper_8.pdf)

* 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](https://dl.acm.org/doi/abs/10.1145/3347146.3359094) [Code](https://github.com/TobiasSkovgaardJepsen/relational-fusion-networks)

* 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](https://www.ijcai.org/Proceedings/2019/0264) [Code](https://github.com/nnzhan/Graph-WaveNet)

* 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](https://www.ijcai.org/Proceedings/2019/0317)

* 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](https://ieeexplore.ieee.org/abstract/document/8917213/)

* 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](https://ieeexplore.ieee.org/abstract/document/9060351)

* 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](https://ieeexplore.ieee.org/abstract/document/8917509/)

* 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](https://ieeexplore.ieee.org/abstract/document/8917174/) [Code](https://github.com/mawright/trafficgraphnn)

* 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](https://ieeexplore.ieee.org/abstract/document/8917502/)

* 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](https://dl.acm.org/doi/abs/10.1145/3292500.3330877)

* 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](https://ieeexplore.ieee.org/abstract/document/9005965/)

* 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](https://ieeexplore.ieee.org/abstract/document/9005986/)

* 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](https://arxiv.org/abs/1904.06316)

* 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](https://dl.acm.org/doi/abs/10.1145/3357384.3358097)

* 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](https://www.aaai.org/ojs/index.php/AAAI/article/view/4247)

* 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](https://www.ijcai.org/Proceedings/2019/0274)

* 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](https://ieeexplore.ieee.org/abstract/document/8788749/)

* 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](https://ieeexplore.ieee.org/abstract/document/9060248)

* 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](https://link.springer.com/chapter/10.1007/978-3-030-33607-3_23)

* 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](https://dl.acm.org/doi/abs/10.1145/3292500.3330884) [Code](https://github.com/panzheyi/ST-MetaNet)

## 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](https://arxiv.org/abs/1903.00919)

* 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](https://arxiv.org/abs/1904.06656)

* 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](https://arxiv.org/abs/1905.10709) [Code](https://github.com/LeeDoYup/TGNet-keras)

* Lee K, Rhee W. Graph Convolutional Modules for Traffic Forecasting[J]. arXiv preprint arXiv:1905.12256, 2019. [Link](https://arxiv.org/abs/1905.12256)

* 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](https://arxiv.org/abs/1903.06261)

* Shleifer S, McCreery C, Chitters V. Incrementally Improving Graph WaveNet Performance on Traffic Prediction[J]. arXiv preprint arXiv:1912.07390, 2019. [Link](https://arxiv.org/abs/1912.07390) [Code](https://github.com/sshleifer/Graph-WaveNet)

* 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](https://arxiv.org/abs/1905.11395)

* Zhou X, Shen Y, Huang L. Revisiting Flow Information for Traffic Prediction[J]. arXiv preprint arXiv:1906.00560, 2019. [Link](https://arxiv.org/abs/1906.00560)

* 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](https://arxiv.org/abs/1903.05631)

# 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](https://www.sciencedirect.com/science/article/pii/S0968090X18300974)

## 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](https://dl.acm.org/doi/abs/10.1145/3274895.3274896) [Code](https://github.com/Di-Chai/GraphCNN-Bike)

* 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](https://dl.acm.org/doi/abs/10.1145/3219819.3219895) [Code](https://github.com/JingqingZ/BaiduTraffic)

* Li Y, Yu R, Shahabi C, Liu Y, Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting[C], ICLR 2018. [Link](https://openreview.net/pdf?id=SJiHXGWAZ) [Code-tensorflow](https://github.com/liyaguang/DCRNN) [Code-pytorch](https://github.com/chnsh/DCRNN_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](http://auai.org/uai2018/proceedings/papers/139.pdf) [Code](https://github.com/jennyzhang0215/GaAN)

* 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](https://ieeexplore.ieee.org/document/8612556)

* 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](https://milets18.github.io/papers/milets18_paper_6.pdf)

* 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](https://ieeexplore.ieee.org/abstract/document/8560019/) [Code](https://github.com/RingBDStack/GCNN-In-Traffic)

* 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](http://www.mlgworkshop.org/2018/papers/MLG2018_paper_41.pdf) [Code](https://github.com/sudatta0993/Dynamic-Congestion-Prediction)

* 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](https://ieeexplore.ieee.org/abstract/document/8545106/)

* 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](https://www.ijcai.org/Proceedings/2018/0505) [Code1](https://github.com/VeritasYin/STGCN_IJCAI-18) [Code2](https://github.com/Davidham3/STGCN) [Code3](https://github.com/PKUAI26/STGCN-IJCAI-18)

## 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](https://arxiv.org/abs/1811.00740) [Code](https://github.com/xxArbiter/grnn)

* 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](https://arxiv.org/abs/1811.05157)