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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:

Train delay analysis and prediction based on big data fusion
Pu Wang, Qingpeng Zhang
Transportation Safety and Environment (2019) Vol. 1, Iss. 1, pp. 79-88
Open Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

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
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
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
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
Predicting primary delay of train services using graph-embedding based machine learning
Ruifan Tang, Ronghui Liu, Zhiyuan Lin
Journal of Rail Transport Planning & Management (2025) Vol. 34, pp. 100518-100518
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
Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions
Ana I. Torre-Bastida, Josu Díaz-de-Arcaya, Eneko Osaba, et al.
Neural Computing and Applications (2021)
Open Access | Times Cited: 28
Traffic State Data Imputation: An Efficient Generating Method Based on the Graph Aggregator
Dongwei Xu, Peng Hang, Chenchen Wei, et al.
IEEE Transactions on Intelligent Transportation Systems (2021) Vol. 23, Iss. 8, pp. 13084-13093
Closed Access | Times Cited: 23
A semi-supervised co-training model for predicting passenger flow change in expanding subways
Kaipeng Wang, Bao Guo, Hu Yang, et al.
Expert Systems with Applications (2022) Vol. 209, pp. 118310-118310
Closed Access | Times Cited: 16
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
Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes
Pipatphon Lapamonpinyo, Sybil Derrible, Francesco Corman
IEEE Open Journal of Intelligent Transportation Systems (2022) Vol. 3, pp. 539-550
Open Access | Times Cited: 14
Railway Delay Prediction with Spatial-Temporal Graph Convolutional Networks
Jacob Scott White Heglund, Panukorn Taleongpong, Simon Hu, et al.
(2020), pp. 1-6
Closed Access | Times Cited: 22
Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models
Kah Yong Tiong, Zhenliang Ma, Carl-William Palmqvist
Transportation Research Part A Policy and Practice (2023) Vol. 174, pp. 103751-103751
Open Access | Times Cited: 7
Quantifying weather-induced unreliable public transportation service in cold regions under future climate model scenarios
Xuelin Tian, Chen Lu, Ziyang Song, et al.
Sustainable Cities and Society (2024) Vol. 113, pp. 105660-105660
Open Access | Times Cited: 2
Punctuality development and delay explanation factors on Norwegian railways in the period 2005–2014
Andreas Økland, Nils O.E. Olsson
Public Transport (2020) Vol. 13, Iss. 1, pp. 127-161
Open Access | Times Cited: 19
Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network
Panukorn Taleongpong, Simon Hu, Zhoutong Jiang, et al.
Journal of Intelligent Transportation Systems (2020) Vol. 26, Iss. 3, pp. 311-329
Open Access | Times Cited: 18
Artificial Intelligence Applied on Traffic Planning and Management for Rail Transport: A Review and Perspective
Jiamin Zhang, Jiarui Zhang
Discrete Dynamics in Nature and Society (2023) Vol. 2023, pp. 1-17
Open Access | Times Cited: 6
The Bounds of Improvements Toward Real-Time Forecast of Multi-Scenario Train Delays
Jianqing Wu, Yihui Wang, Bo Du, et al.
IEEE Transactions on Intelligent Transportation Systems (2021) Vol. 23, Iss. 3, pp. 2445-2456
Closed Access | Times Cited: 15
A Global Modeling Pruning Ensemble Stacking With Deep Learning and Neural Network Meta-Learner for Passenger Train Delay Prediction
Veronica A. Boateng, Bo Yang
IEEE Access (2023) Vol. 11, pp. 62605-62615
Open Access | Times Cited: 4
Statistical learning for train delays and influence of winter climate and atmospheric icing
Jianfeng Wang, Roberto Mantas-Nakhai, Jun Yu
Journal of Rail Transport Planning & Management (2023) Vol. 26, pp. 100388-100388
Open Access | Times Cited: 4
Examining the characteristics between time and distance gaps of secondary crashes
Xinyuan Liu, Jinjun Tang, Yuan Chen, et al.
Transportation Safety and Environment (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 4
A delay prediction model for high-speed railway: an extreme learning machine tuned via particle swarm optimization
Yanqiu Li, Xinyue Xu, Jianmin Li, et al.
(2020), pp. 1-5
Closed Access | Times Cited: 9
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