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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:

Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow
Yuxin He, Lishuai Li, Xinting Zhu, et al.
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 10, pp. 18155-18174
Open Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 36, Iss. 10, pp. 5388-5408
Open Access | Times Cited: 134
A Flow Feedback Traffic Prediction Based on Visual Quantified Features
Jing Chen, Mengqi Xu, Wenqiang Xu, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 9, pp. 10067-10075
Closed Access | Times Cited: 96
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: 81
Learning spatial–temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit
Jinxin Wu, Deqiang He, Zhenzhen Jin, et al.
Expert Systems with Applications (2024) Vol. 245, pp. 123091-123091
Closed Access | Times Cited: 8
Arrival information-guided spatiotemporal prediction of transportation hub passenger distribution
Long Cheng, Xinmei Cai, Da Lei, et al.
Transportation Research Part E Logistics and Transportation Review (2025) Vol. 195, pp. 104011-104011
Closed Access | Times Cited: 1
Spatial-Temporal Graph Convolutional-Based Recurrent Network for Electric Vehicle Charging Stations Demand Forecasting in Energy Market
Hyung Joon Kim, Mun-Kyeom Kim
IEEE Transactions on Smart Grid (2024) Vol. 15, Iss. 4, pp. 3979-3993
Closed Access | Times Cited: 7
FASTNN: A Deep Learning Approach for Traffic Flow Prediction Considering Spatiotemporal Features
Qianqian Zhou, Nan Chen, Siwei Lin
Sensors (2022) Vol. 22, Iss. 18, pp. 6921-6921
Open Access | Times Cited: 23
Forecasting Short-Term Passenger Flow of Subway Stations Based on the Temporal Pattern Attention Mechanism and the Long Short-Term Memory Network
Lingxiang Wei, Dongjun Guo, Zhilong Chen, et al.
ISPRS International Journal of Geo-Information (2023) Vol. 12, Iss. 1, pp. 25-25
Open Access | Times Cited: 16
IG-Net: An Interaction Graph Network Model for Metro Passenger Flow Forecasting
Zhiyuan Liu, Sheng Wang, Hantao Zhao, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 4, pp. 4147-4157
Closed Access | Times Cited: 15
Deep Learning for Metro Short-Term Origin-Destination Passenger Flow Forecasting Considering Section Capacity Utilization Ratio
Yan Zhang, KeYang Sun, Di Wen, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 8, pp. 7943-7960
Closed Access | Times Cited: 15
Machine Learning for public transportation demand prediction: A Systematic Literature Review
Franca Rocco di Torrepadula, Enea Vincenzo Napolitano, Sergio Di Martino, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 137, pp. 109166-109166
Closed Access | Times Cited: 5
Traffic Flow Prediction Based on Hybrid Deep Learning Models Considering Missing Data and Multiple Factors
Wenbao Zeng, Ketong Wang, Jianghua Zhou, et al.
Sustainability (2023) Vol. 15, Iss. 14, pp. 11092-11092
Open Access | Times Cited: 12
Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
Pritam Bikram, Shubhajyoti Das, Arindam Biswas
Applied Intelligence (2024) Vol. 54, Iss. 3, pp. 2716-2749
Closed Access | Times Cited: 4
Identifying, Analyzing, and forecasting commuting patterns in urban public Transportation: A review
Jingwen Xiong, Lunhui Xu, Zhuoyan Wei, et al.
Expert Systems with Applications (2024) Vol. 249, pp. 123646-123646
Closed Access | Times Cited: 4
Forecasting short-term passenger flow via CBGC-SCI: an in-depth comparative study on Shenzhen Metro
Yuxin He, Weihang Hong, Lishuai Li, et al.
Machine Learning (2025) Vol. 114, Iss. 1
Closed Access
Transformer-based short-term traffic forecasting model considering traffic spatiotemporal correlation
Ande Chang, Yuting Ji, Yiming Bie
Frontiers in Neurorobotics (2025) Vol. 19
Open Access
TMS-GNN: Traffic-aware Multistep Graph Neural Network for bus passenger flow prediction
Asiye Baghbani, Saeed Rahmani, Nizar Bouguila, et al.
Transportation Research Part C Emerging Technologies (2025) Vol. 174, pp. 105107-105107
Open Access
A method for short-term passenger flow prediction in urban rail transit based on deep learning
Ningning Dong, Tiezhu Li, Tianhao Liu, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 22, pp. 61621-61643
Closed Access | Times Cited: 10
A review of research methods on the coupling relationship between urban rail transit and urban space: revealing spatiotemporal relationships through big data
Zishuo Liu, Haishan Xia, Tong Zhang
International Journal of Digital Earth (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 3
Forecasting network-wide multi-step metro ridership with an attention-weighted multi-view graph to sequence learning approach
Jie Bao, Jiawei Kang, Zhao Yang, et al.
Expert Systems with Applications (2022) Vol. 210, pp. 118475-118475
Closed Access | Times Cited: 15
A deep neural network model with GCN and 3D convolutional network for short‐term metro passenger flow forecasting
Xuanrong Zhang, Cheng Wang, Jianwei Chen, et al.
IET Intelligent Transport Systems (2023) Vol. 17, Iss. 8, pp. 1599-1607
Open Access | Times Cited: 9
The HWAM-EMD-GRU Forecasting Model for Short-Term Passenger Flow in an Airport Light Rail Transit Line
Qian Qin, Ziji’an Wang, Bing Li, et al.
Urban Rail Transit (2024) Vol. 10, Iss. 2, pp. 178-187
Open Access | Times Cited: 2
Designing on-board explainable passenger flow prediction
Mario Barbareschi, Antonio Emmanuele, Nicola Mazzocca, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 139, pp. 109648-109648
Closed Access | Times Cited: 2
Large-Scale Origin–Destination Prediction for Urban Rail Transit Network Based on Graph Convolutional Neural Network
Xuemei Wang, Yunlong Zhang, Jinlei Zhang
Sustainability (2024) Vol. 16, Iss. 23, pp. 10190-10190
Open Access | Times Cited: 2
Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis
Qianru Qi, Rongjun Cheng, Hongxia Ge
Digital Transportation and Safety (2023) Vol. 2, Iss. 1, pp. 12-22
Open Access | Times Cited: 6
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