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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:

Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network
Shengyou Wang, Anthony Chen, Pinxi Wang, et al.
Transportation Research Part C Emerging Technologies (2023) Vol. 153, pp. 104205-104205
Closed Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework
Zhanyu Feng, Jian Zhang, Han Jiang, et al.
Energy (2024) Vol. 302, pp. 131780-131780
Closed Access | Times Cited: 23
Electric vehicle charging load forecasting considering weather impact
Wenhao Wang, Aihong Tang, Feng Wei, et al.
Applied Energy (2025) Vol. 383, pp. 125337-125337
Closed Access | Times Cited: 3
KAN–CNN: A Novel Framework for Electric Vehicle Load Forecasting with Enhanced Engineering Applicability and Simplified Neural Network Tuning
Zhigang Pei, Zhiyuan Zhang, Jiaming Chen, et al.
Electronics (2025) Vol. 14, Iss. 3, pp. 414-414
Open Access | Times Cited: 2
An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing
Lei Zhang, Zhijia Huang, Zhenpo Wang, et al.
Energy (2024) Vol. 299, pp. 130844-130844
Closed Access | Times Cited: 15
A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing
H. H. Kuang, Haohao Qu, Kunxiang Deng, et al.
Applied Energy (2024) Vol. 363, pp. 123059-123059
Closed Access | Times Cited: 15
A Physics-Informed and Attention-Based Graph Learning Approach for Regional Electric Vehicle Charging Demand Prediction
Haohao Qu, H. H. Kuang, Qiuxuan Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 10, pp. 14284-14297
Open Access | Times Cited: 10
An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction
Shengyou Wang, Yuan Li, Chunfu Shao, et al.
Applied Energy (2025) Vol. 383, pp. 125320-125320
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
Research on charging demands of commercial electric vehicles based on Voronoi diagram and spatial econometrics model: An empirical study in Chongqing China
Chunyan Shuai, Xiaoqi Zhang, Xin Ouyang, et al.
Sustainable Cities and Society (2024) Vol. 105, pp. 105335-105335
Closed Access | Times Cited: 7
An Intelligent Deep Learning Framework for Traffic Flow Imputation and Short-term Prediction Based on Dynamic Features
Xianhui Zong, Yong Qi, He Yan, et al.
Knowledge-Based Systems (2024) Vol. 300, pp. 112178-112178
Closed Access | Times Cited: 6
Data poisoning attacks in intelligent transportation systems: A survey
Feilong Wang, Xin Wang, Xuegang Ban
Transportation Research Part C Emerging Technologies (2024) Vol. 165, pp. 104750-104750
Closed Access | Times Cited: 6
Enhancing the utilization of renewable generation on the highway with mobile energy storage vehicles and electric vehicles
Dawei Wang, Hongke Xu, Liang Dai, et al.
Electric Power Systems Research (2024) Vol. 231, pp. 110311-110311
Closed Access | Times Cited: 5
Uncertainty-aware probabilistic graph neural networks for road-level traffic crash prediction
Xiaowei Gao, Xinke Jiang, James Haworth, et al.
Accident Analysis & Prevention (2024) Vol. 208, pp. 107801-107801
Open Access | Times Cited: 5
The role of EV fast charging in the urban context: An agent-based model approach
F. Hipolito, Jeppe Rich, Peter Bach Andersen
eTransportation (2024) Vol. 22, pp. 100369-100369
Closed Access | Times Cited: 4
Empirical analysis of intelligent charging Decisions: Boosting efficiency for electric trucks
Qiujun Qian, Mi Gan, Xiaoyuan Yang
Transportation Research Part D Transport and Environment (2025) Vol. 139, pp. 104572-104572
Closed Access
Research on Site Selection and Capacity Determination of Electric Vehicle Charging Stations in Urban Commercial Areas
Lei Wen, Xinyi Shen, Wei Xue
(2025)
Closed Access
Quantifying the Uncertainty of Electric Vehicle Charging with Probabilistic Load Forecasting
Yvenn Amara-Ouali, Bachir Hamrouche, Guillaume Principato, et al.
World Electric Vehicle Journal (2025) Vol. 16, Iss. 2, pp. 88-88
Open Access
A hybrid Bayesian network-based deep learning approach combining climatic and reliability factors to forecast electric vehicle charging capacity
David Chunhu Li
Heliyon (2025) Vol. 11, Iss. 4, pp. e42483-e42483
Open Access
Electric vehicle charging demand forecasting at charging stations under climate influence for electricity dispatching
Peilu Chen, Jianzhong Qin, Jinxi Dong, et al.
IET Power Electronics (2025)
Open Access
Urban public charging infrastructure planning for electric vehicles: A continuum approximation approach
Yichan An, Joseph Y.J. Chow, Soomin Woo, et al.
Transportation Research Part D Transport and Environment (2025) Vol. 141, pp. 104605-104605
Open Access
Citywide electric vehicle charging demand prediction approach considering urban region and dynamic influences
H. H. Kuang, Kunxiang Deng, Linlin You, et al.
Energy (2025), pp. 135170-135170
Closed Access
A Physics-Informed Machine Learning Framework for Speed-Flow Prediction: Integrating an S-Shaped Traffic Stream Model with Deep Learning Models
Feng Shao, Hu Shao, Xin Wu, et al.
(2025)
Closed Access
UrbanEV: An Open Benchmark Dataset for Urban Electric Vehicle Charging Demand Prediction
Han Li, Haohao Qu, Xiaojun Tan, et al.
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
Analysis of Charging Demands, Influencing Factors and Spatial Effects of Electric Vehicles Based on Multi-source Data and Local Spatial Models
Xiaoqi Zhang, Fang Yang, Chunyan Shuai, et al.
Sustainable Cities and Society (2025), pp. 106371-106371
Closed Access
High-resolution simulation and prediction of urban private vehicles energy consumption system: Agent-based modelling
Chuang Tu, Jiayi Liu, Jing Wang, et al.
International Journal of Electrical Power & Energy Systems (2025) Vol. 168, pp. 110669-110669
Closed Access
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