Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Graph neural network for traffic forecasting: A survey
Weiwei Jiang, Jiayun Luo
Expert Systems with Applications (2022) Vol. 207, pp. 117921-117921
Open Access | Times Cited: 751

Showing 26-50 of 751 citing articles:

Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction
Zhao Liu, Fan Ding, Yunqi Dai, et al.
Expert Systems with Applications (2024) Vol. 249, pp. 123543-123543
Closed Access | Times Cited: 17
Smart virtual sensing for deep excavations using real-time ensemble graph neural networks
Chen Yang, Chen Wang, Feng Zhao, et al.
Automation in Construction (2025) Vol. 172, pp. 106040-106040
Closed Access | Times Cited: 2
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Eric L. Manibardo, Ibai Laña, Javier Del Ser
IEEE Transactions on Intelligent Transportation Systems (2021) Vol. 23, Iss. 7, pp. 6164-6188
Open Access | Times Cited: 83
Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction
Yanfeng Sun, Xiangheng Jiang, Yongli Hu, et al.
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 12, pp. 23680-23693
Closed Access | Times Cited: 60
A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction
Yong Chen, Xiqun Chen
Transportation Research Part C Emerging Technologies (2022) Vol. 143, pp. 103820-103820
Closed Access | Times Cited: 57
Hybrid deep learning models for traffic prediction in large-scale road networks
Zheng Ge, Wei Koong Chai, Jing‐Lin Duanmu, et al.
Information Fusion (2022) Vol. 92, pp. 93-114
Open Access | Times Cited: 51
Evaluation of Vision Transformers for Traffic Sign Classification
Yuping Zheng, Weiwei Jiang
Wireless Communications and Mobile Computing (2022) Vol. 2022, pp. 1-14
Open Access | Times Cited: 42
Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
Weiwei Jiang, Jiayun Luo
Applied System Innovation (2022) Vol. 5, Iss. 1, pp. 23-23
Open Access | Times Cited: 41
Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
Yuebing Liang, Guan Huang, Zhan Zhao
Transportation Research Part C Emerging Technologies (2022) Vol. 140, pp. 103731-103731
Open Access | Times Cited: 41
Survey of Graph Neural Networks and Applications
Fan Liang, Cheng Qian, Wei Yu, et al.
Wireless Communications and Mobile Computing (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 39
RPConvformer: A novel Transformer-based deep neural networks for traffic flow prediction
Yanjie Wen, Ping Xu, Zhihong Li, et al.
Expert Systems with Applications (2023) Vol. 218, pp. 119587-119587
Closed Access | Times Cited: 37
Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation
Gabriel O. Ferreira, Chiara Ravazzi, Fabrizio Dabbene, et al.
IEEE Access (2023) Vol. 11, pp. 6018-6044
Open Access | Times Cited: 36
Similarity-navigated graph neural networks for node classification
Minhao Zou, Zhongxue Gan, Ruizhi Cao, et al.
Information Sciences (2023) Vol. 633, pp. 41-69
Open Access | Times Cited: 34
Attention-based graph neural networks: a survey
Chengcheng Sun, Chenhao Li, Xiang Lin, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. S2, pp. 2263-2310
Closed Access | Times Cited: 33
Edge Computation Offloading With Content Caching in 6G-Enabled IoV
Xuanhong Zhou, Muhammad Bilal, Ruihan Dou, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 25, Iss. 3, pp. 2733-2747
Closed Access | Times Cited: 32
FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network
Tao Qi, Lingqiang Chen, Guanghui Li, et al.
Applied Soft Computing (2023) Vol. 138, pp. 110175-110175
Closed Access | Times Cited: 32
Traffic Prediction With Missing Data: A Multi-Task Learning Approach
Ao Wang, Yongchao Ye, Xiaozhuang Song, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 4, pp. 4189-4202
Closed Access | Times Cited: 31
Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data
Xinqi Zhang, Jihao Shi, Xinyan Huang, et al.
Expert Systems with Applications (2023) Vol. 231, pp. 120542-120542
Open Access | Times Cited: 31
End-to-end wind turbine wake modelling with deep graph representation learning
Siyi Li, Mingrui Zhang, Matthew D. Piggott
Applied Energy (2023) Vol. 339, pp. 120928-120928
Open Access | Times Cited: 29
LLM Multimodal Traffic Accident Forecasting
I. de Zarzà, J. de Curtò, Gemma Roig, et al.
Sensors (2023) Vol. 23, Iss. 22, pp. 9225-9225
Open Access | Times Cited: 28
Bibliometric methods in traffic flow prediction based on artificial intelligence
Yong Chen, Wanru Wang, Xiqun Chen
Expert Systems with Applications (2023) Vol. 228, pp. 120421-120421
Closed Access | Times Cited: 27
Graph neural networks for construction applications
Yilong Jia, Jun Wang, Wenchi Shou, et al.
Automation in Construction (2023) Vol. 154, pp. 104984-104984
Closed Access | Times Cited: 26
Multiple Information Spatial–Temporal Attention based Graph Convolution Network for traffic prediction
Shiming Tao, Huyin Zhang, Fei Yang, et al.
Applied Soft Computing (2023) Vol. 136, pp. 110052-110052
Closed Access | Times Cited: 25
Semantics-Aware Dynamic Graph Convolutional Network for Traffic Flow Forecasting
Guojun Liang, U Kintak, Xin Ning, et al.
IEEE Transactions on Vehicular Technology (2023) Vol. 72, Iss. 6, pp. 7796-7809
Open Access | Times Cited: 23
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
Hongwu Peng, Xi Xie, Kaustubh Shivdikar, et al.
(2024), pp. 683-698
Open Access | Times Cited: 15

Scroll to top