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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:

Modeling train operation as sequences: A study of delay prediction with operation and weather data
Ping Huang, Chao Wen, Liping Fu, et al.
Transportation Research Part E Logistics and Transportation Review (2020) Vol. 141, pp. 102022-102022
Closed Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization
Rui Shi, Xinyue Xu, Jianmin Li, et al.
Applied Soft Computing (2021) Vol. 109, pp. 107538-107538
Closed Access | Times Cited: 149
A literature review of Artificial Intelligence applications in railway systems
Ruifan Tang, Lorenzo De Donato, Nikola Bešinović, et al.
Transportation Research Part C Emerging Technologies (2022) Vol. 140, pp. 103679-103679
Open Access | Times Cited: 147
Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data
Wen Zhang, Shaoshan Yan, Jian Li, et al.
Transportation Research Part E Logistics and Transportation Review (2022) Vol. 158, pp. 102611-102611
Closed Access | Times Cited: 68
Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction
Jiateng Yin, Chenhe Ning, Tao Tang
Information Sciences (2022) Vol. 600, pp. 377-400
Closed Access | Times Cited: 45
A review of train delay prediction approaches
Thomas Spanninger, Alessio Trivella, Beda Büchel, et al.
Journal of Rail Transport Planning & Management (2022) Vol. 22, pp. 100312-100312
Open Access | Times Cited: 41
A review of data-driven approaches to predict train delays
Kah Yong Tiong, Zhenliang Ma, Carl-William Palmqvist
Transportation Research Part C Emerging Technologies (2023) Vol. 148, pp. 104027-104027
Open Access | Times Cited: 34
Explainable train delay propagation: A graph attention network approach
Ping Huang, Jingwei Guo, Shuming Liu, et al.
Transportation Research Part E Logistics and Transportation Review (2024) Vol. 184, pp. 103457-103457
Closed Access | Times Cited: 10
An instance-based transfer learning model with attention mechanism for freight train travel time prediction in the China–Europe railway express
Jingwei Guo, Wei Wang, Jiayi Guo, et al.
Expert Systems with Applications (2024) Vol. 251, pp. 123989-123989
Closed Access | Times Cited: 8
Enhancing the Understanding of Train Delays With Delay Evolution Pattern Discovery: A Clustering and Bayesian Network Approach
Ping Huang, Thomas Spanninger, Francesco Corman
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 9, pp. 15367-15381
Closed Access | Times Cited: 33
Railway network delay evolution: A heterogeneous graph neural network approach
Zhongcan Li, Ping Huang, Chao Wen, et al.
Applied Soft Computing (2024) Vol. 159, pp. 111640-111640
Open Access | Times Cited: 5
Prediction of the estimated times of arrival of freight train based on operational and geospatial features
Masoud Yaghini, Amirhosein Ezati
Journal of Rail Transport Planning & Management (2025) Vol. 34, pp. 100508-100508
Closed Access
Forecasting train travel times of China–Europe Railway Express through a hybrid deep learning model optimized with a bandit-based approach
Yongxiang Zhang, Liting Gu, Jingwei Guo, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 150, pp. 110552-110552
Closed Access
Modeling train timetables as images: A cost-sensitive deep learning framework for delay propagation pattern recognition
Ping Huang, Zhongcan Li, Chao Wen, et al.
Expert Systems with Applications (2021) Vol. 177, pp. 114996-114996
Closed Access | Times Cited: 37
Prediction of train arrival delays considering route conflicts at multi-line stations
Zhongcan Li, Ping Huang, Chao Wen, et al.
Transportation Research Part C Emerging Technologies (2022) Vol. 138, pp. 103606-103606
Open Access | Times Cited: 26
An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree
Dalin Zhang, Yi Xu, Yunjuan Peng, et al.
IEEE Transactions on Fuzzy Systems (2022) Vol. 31, Iss. 2, pp. 421-433
Open Access | Times Cited: 23
Real-time High-Speed Train Delay Prediction using Seemingly Unrelated Regression Models
Kah Yong Tiong, Zhenliang Ma, Carl-William Palmqvist
Transportation research procedia (2025) Vol. 82, pp. 271-278
Open Access
The Impact of Switch Faults on Train Delays: A Case Study of the Swedish Railway Network
Grace Mukunzi, Carl-William Palmqvist
Transportation research procedia (2025) Vol. 82, pp. 390-403
Open Access
AP-GRIP evaluation framework for data-driven train delay prediction models: systematic literature review
Kah Yong Tiong, Zhenliang Ma, Carl-William Palmqvist
European Transport Research Review (2025) Vol. 17, Iss. 1
Open Access
Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach
Xu Bao, Yanqiu Li, Jianmin Li, et al.
Journal of Advanced Transportation (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 29
Train traffic control in merging stations: A data-driven approach
Ping Huang, Zhongcan Li, Yongqiu Zhu, et al.
Transportation Research Part C Emerging Technologies (2023) Vol. 152, pp. 104155-104155
Closed Access | Times Cited: 12
Quantifying the dynamic predictability of train delay with uncertainty-aware neural networks
Thomas Spanninger, Nina Wiedemann, Francesco Corman
Transportation Research Part C Emerging Technologies (2024) Vol. 162, pp. 104563-104563
Open Access | Times Cited: 3
A novel deep learning model for short-term train delay prediction
Bowen Gao, Lei Zhang, Dongxiu Ou, et al.
Information Sciences (2023) Vol. 645, pp. 119270-119270
Closed Access | Times Cited: 9
Prediction of rail transit delays with machine learning: How to exploit open data sources
Malek Sarhani, Stefan Voß
Multimodal Transportation (2024) Vol. 3, Iss. 2, pp. 100120-100120
Open Access | Times Cited: 3
Data-driven decision support for rail traffic control: A predictive approach
Jie Luo, Qiyuan Peng, Chao Wen, et al.
Expert Systems with Applications (2022) Vol. 207, pp. 118050-118050
Open Access | Times Cited: 15
Identifying the rail operating features associated to intermodal freight rail operation delays
Juan Pineda-Jaramillo, Francesco Viti
Transportation Research Part C Emerging Technologies (2022) Vol. 147, pp. 103993-103993
Closed Access | Times Cited: 15
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