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OpenAlex Citation Counts

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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:

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: 82

Showing 1-25 of 82 citing articles:

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: 729
Long sequence time-series forecasting with deep learning: A survey
Zonglei Chen, Minbo Ma, Tianrui Li, et al.
Information Fusion (2023) Vol. 97, pp. 101819-101819
Closed Access | Times Cited: 102
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Maryam Shaygan, Collin Meese, Wanxin Li, et al.
Transportation Research Part C Emerging Technologies (2022) Vol. 145, pp. 103921-103921
Open Access | Times Cited: 99
Adaptive Spatio-temporal Graph Neural Network for traffic forecasting
Xuxiang Ta, Zihan Liu, Xiao Hu, et al.
Knowledge-Based Systems (2022) Vol. 242, pp. 108199-108199
Closed Access | Times Cited: 97
Graph Neural Networks for Intelligent Transportation Systems: A Survey
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 8, pp. 8846-8885
Open Access | Times Cited: 73
Graph Neural Network for Traffic Forecasting: The Research Progress
Weiwei Jiang, Jiayun Luo, Miao He, et al.
ISPRS International Journal of Geo-Information (2023) Vol. 12, Iss. 3, pp. 100-100
Open Access | Times Cited: 66
Evaluation of post-hoc interpretability methods in time-series classification
Hugues Turbé, Mina Bjelogrlic, Christian Lovis, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 3, pp. 250-260
Open Access | Times Cited: 44
Traffic management approaches using machine learning and deep learning techniques: A survey
Hanan Muqbil Almukhalfi, Ayman Noor, Talal H. Noor
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108147-108147
Closed Access | Times Cited: 23
A comprehensive study of speed prediction in transportation system: From vehicle to traffic
Zewei Zhou, Ziru Yang, Yuanjian Zhang, et al.
iScience (2022) Vol. 25, Iss. 3, pp. 103909-103909
Open Access | Times Cited: 45
Graph Construction for Traffic Prediction: A Data-Driven Approach
James J. Q. Yu
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 9, pp. 15015-15027
Closed Access | Times Cited: 43
DyGCN-LSTM: A dynamic GCN-LSTM based encoder-decoder framework for multistep traffic prediction
Rahul Kumar, João Mendes Moreira, Joydeep Chandra
Applied Intelligence (2023) Vol. 53, Iss. 21, pp. 25388-25411
Closed Access | Times Cited: 22
Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow
Karla L. Martínez‐Mauricio, César R. García‐Jacas, Greneter Cordoves‐Delgado
Protein Science (2024) Vol. 33, Iss. 4
Closed Access | Times Cited: 8
Do deep learning models make a difference in the identification of antimicrobial peptides?
César R. García‐Jacas, Sergio A. Pinacho-Castellanos, Luis A. García‐González, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 32
Revolutionizing Urban Traffic Mobility With Graph Neural Networks-Driven Intelligent Transportation Systems (ITS)
G. Arun Sampaul Thomas, S. Muthukaruppasamy, P. Deivendran, et al.
IGI Global eBooks (2025), pp. 317-342
Closed Access
A Novel Multi-task Learning Framework for Predicting Traffic Congestion
Charan Teja Marrimanu, Arjun Chakravarthi Pogaku
Lecture notes in computer science (2025), pp. 200-216
Closed Access
Traffic demand prediction using a social multiplex networks representation on a multimodal and multisource dataset
Panagiotis Fafoutellis, Eleni I. Vlahogianni
International Journal of Transportation Science and Technology (2024) Vol. 14, pp. 171-185
Open Access | Times Cited: 4
Dynamic Spatio-Temporal Residual Hypergraph Convolutional Networks for Traffic Flow Forecasting
Jun Su, Hairu Wang, Кrzysztof Przystupa, et al.
Transportation Research Record Journal of the Transportation Research Board (2025)
Closed Access
Small-scale self-driving cars: A systematic literature review
Felipe Caleffi, Lauren da Silva Rodrigues, Joice da Silva Stamboroski, et al.
Journal of Traffic and Transportation Engineering (English Edition) (2024) Vol. 11, Iss. 2, pp. 271-292
Open Access | Times Cited: 3
Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods
Fernando José Braz, João C. Ferreira, Francisco Gonçalves, et al.
Sensors (2022) Vol. 22, Iss. 12, pp. 4485-4485
Open Access | Times Cited: 16
Are Graphs and GCNs necessary for short-term metro ridership forecasting?
Qiong Yang, Xianghua Xu, Z. G. Wang, et al.
Expert Systems with Applications (2024) Vol. 254, pp. 124431-124431
Closed Access | Times Cited: 3
A Review of Intelligent Configuration and Its Security for Complex Networks
Yue Zhao, Bin Yang, Fei Teng, et al.
Chinese Journal of Electronics (2024) Vol. 33, Iss. 4, pp. 920-947
Closed Access | Times Cited: 3
Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant?
César R. García‐Jacas, Luis A. García‐González, Félix Martínez-Rios, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 6
Closed Access | Times Cited: 14
Unlocking the Full Potential of Deep Learning in Traffic Forecasting Through Road Network Representations: A Critical Review
Panagiotis Fafoutellis, Eleni I. Vlahogianni
Data Science for Transportation (2023) Vol. 5, Iss. 3
Open Access | Times Cited: 8
Context-aware multi-head self-attentional neural network model for next location prediction
Ye Hong, Yatao Zhang, Konrad Schindler, et al.
Transportation Research Part C Emerging Technologies (2023) Vol. 156, pp. 104315-104315
Open Access | Times Cited: 7
Predicting traffic intensity in the urban area of Madrid: Integrating route network topology into a machine-learning model
Julián Moral Carcedo
Engineering Applications of Artificial Intelligence (2024) Vol. 137, pp. 109154-109154
Open Access | Times Cited: 2
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