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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 Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests
Mohammad Amin Nabian, Negin Alemazkoor, Hadi Meidani
Transportation Research Record Journal of the Transportation Research Board (2019) Vol. 2673, Iss. 5, pp. 564-573
Closed Access | Times Cited: 52

Showing 1-25 of 52 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
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
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 ensemble prediction model for train delays
Rahul Nair, Thanh Lam Hoang, Marco Laumanns, et al.
Transportation Research Part C Emerging Technologies (2019) Vol. 104, pp. 196-209
Closed Access | Times Cited: 72
A Bayesian network model to predict the effects of interruptions on train operations
Ping Huang, Javad Lessan, Chao Wen, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 114, pp. 338-358
Closed Access | Times Cited: 57
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
Near-term train delay prediction in the Dutch railways network
Zhongcan Li, Chao Wen, Rui Hu, et al.
International Journal of Rail Transportation (2020) Vol. 9, Iss. 6, pp. 520-539
Closed Access | Times Cited: 41
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
A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations
Claudia Bongiovanni, Mor Kaspi, Jean‐François Cordeau, et al.
Transportation Research Part E Logistics and Transportation Review (2022) Vol. 165, pp. 102835-102835
Closed Access | Times Cited: 24
Machine Learning in Urban Rail Transit Systems: A Survey
Li Zhu, Cheng Chen, Hongwei Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 25, Iss. 3, pp. 2182-2207
Closed Access | Times Cited: 14
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
Data-driven insights to reduce uncertainty from disruptive events in passenger railways
Luís Marques, Sérgio Moro, Pedro Ramos
Public Transport (2025)
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
Analyzing network-wide patterns of rail transit delays using Bayesian network learning
Mehmet Baran Ulak, Anil Yazici, Yun Zhang
Transportation Research Part C Emerging Technologies (2020) Vol. 119, pp. 102749-102749
Open Access | Times Cited: 33
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
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
Short-Term Arrival Delay Time Prediction in Freight Rail Operations Using Data-Driven Models
Juan Pineda-Jaramillo, Federico Bigi, Tommaso Bosi, et al.
IEEE Access (2023) Vol. 11, pp. 46966-46978
Open Access | Times Cited: 8
Train delay prediction in Tunisian railway through LightGBM model
Hassiba Laifa, Raoudha Khcherif, Henda Hajjami Ben Ghezalaa
Procedia Computer Science (2021) Vol. 192, pp. 981-990
Open Access | Times Cited: 18
A multi-output deep learning model based on Bayesian optimization for sequential train delays prediction
Jie Luo, Ping Huang, Qiyuan Peng
International Journal of Rail Transportation (2022) Vol. 11, Iss. 5, pp. 705-731
Closed Access | Times Cited: 12
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