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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 and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations
Sheng Dong, Afaq Khattak, Irfan Ullah, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 5, pp. 2925-2925
Open Access | Times Cited: 68

Showing 1-25 of 68 citing articles:

Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review
Yasir Ali, Fizza Hussain, Md. Mazharul Haque
Accident Analysis & Prevention (2023) Vol. 194, pp. 107378-107378
Open Access | Times Cited: 56
Predicting maritime accident risk using Automated Machine Learning
Ziaul Haque Munim, Michael André Sørli, Hyungju Kim, et al.
Reliability Engineering & System Safety (2024) Vol. 248, pp. 110148-110148
Open Access | Times Cited: 26
Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones
Yuan Yuan, Chengwei Li, Xiaolei Geng, et al.
Environment International (2022) Vol. 170, pp. 107574-107574
Open Access | Times Cited: 57
Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering
Seyed Alireza Samerei, Kayvan Aghabayk
Accident Analysis & Prevention (2024) Vol. 202, pp. 107603-107603
Closed Access | Times Cited: 13
Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method
Seyed Alireza Samerei, Kayvan Aghabayk, Alfonso Montella
Safety (2024) Vol. 10, Iss. 1, pp. 22-22
Open Access | Times Cited: 6
Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles
Pavle Dakić, Miodrag Živković, Luka Jovanovic, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations
Umer Mansoor, Arshad Jamal, Junbiao Su, et al.
Transport Policy (2023) Vol. 139, pp. 21-38
Closed Access | Times Cited: 15
Exploring interacting effects of risk factors on run-off-road crash severity: An interpretable machine learning model joint with latent class clustering
Seyed Alireza Samerei, Kayvan Aghabayk
IATSS Research (2024) Vol. 48, Iss. 2, pp. 261-274
Open Access | Times Cited: 5
XGBoost-SHAP and Unobserved Heterogeneity Modelling of Temporal Multivehicle Truck-Involved Crash Severity Patterns
Wimon Laphrom, Chamroeun Se, Thanapong Champahom, et al.
Civil Engineering Journal (2024) Vol. 10, Iss. 6, pp. 1890-1908
Open Access | Times Cited: 5
Advancing autonomous vehicle safety assessment: A novel methodology for moving from functional to concrete scenarios using kinetic 3D-LiDAR and SHAP
Minhee Kang, Saeyan Eom, Keeyeon Hwang
Results in Engineering (2024) Vol. 24, pp. 103364-103364
Closed Access | Times Cited: 5
Predicting Car Accident Severity in Northwest Ethiopia: A Machine Learning Approach Leveraging Driver, Environmental, and Road Conditions
Abraham Keffale Mengistu, Andualem Enyew Gedefaw, Nebebe Demis Baykemagn, et al.
medRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access
Leveraging machine learning to explore nonlinear associations between urban heat vulnerability and morbidity risk
Jiaming Yang, Zhaomin Tong, Jiwei Xu, et al.
Urban Climate (2025) Vol. 59, pp. 102320-102320
Closed Access
Assessing the interdependence of rider fault-status and injury severity in motorcycle rear-end crashes: insights from bivariate probit and XGBoost-SHAP models
Chamroeun Se, Thanapong Champahom, Kestsirin Theerathitichaipa, et al.
International Journal of Injury Control and Safety Promotion (2025), pp. 1-15
Closed Access
Time-Series Prediction of Intense Wind Shear Using Machine Learning Algorithms: A Case Study of Hong Kong International Airport
Afaq Khattak, Pak Wai Chan, Feng Chen, et al.
Atmosphere (2023) Vol. 14, Iss. 2, pp. 268-268
Open Access | Times Cited: 12
Improving the forecast performance of landslide susceptibility mapping by using ensemble gradient boosting algorithms
Hang Ha, Quynh Duy Bui, D. T. Tran, et al.
Environment Development and Sustainability (2024)
Closed Access | Times Cited: 4
Ordinal Random Tree with Rank-Oriented Feature Selection (ORT-ROFS): A Novel Approach for the Prediction of Road Traffic Accident Severity
Bita Ghasemkhani, Kadriye Filiz Balbal, Kökten Ulaş Birant, et al.
Mathematics (2025) Vol. 13, Iss. 2, pp. 310-310
Open Access
An interpretable machine learning framework for enhancing road transportation safety
Ismail Abdulrashid, Wen‐Chyuan Chiang, Jiuh‐Biing Sheu, et al.
Transportation Research Part E Logistics and Transportation Review (2025) Vol. 195, pp. 103969-103969
Closed Access
Integrated Explainable Ensemble Machine Learning Prediction of Injury Severity in Agricultural Accidents
Omer Mermer, Eddie Zhang, İbrahim Demir
medRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access
Stacking models for analyzing traffic injury severity on two-lane, two-way rural roads
Ali Tavakoli Kashani, Parsa Soleyman Farahani, Hamzeh Mansouri Kargar
International Journal of Injury Control and Safety Promotion (2025), pp. 1-12
Closed Access
Analysis of Factors Influencing the Severity of Traffic Crashes by Type of Traffic Crashes on Personal Mobility Using Random Forest Model and SHAP Technique
Yu-Sheng Yang, Su Jin Park, Dong-youn Lee, et al.
International Journal of Highway Engineering (2025) Vol. 27, Iss. 1, pp. 89-99
Closed Access
A Novel Bayesian Optimized-Combined Kernel & Tree Boost Approach for Road Traffic Crash Severity Analysis
Kamran Aziz, Feng Chen, Afaq Khattak
International Journal of Civil Engineering (2025)
Closed Access
Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5
Afaq Khattak, Hamad Almujibah, Ahmed S. Elamary, et al.
Sustainability (2022) Vol. 14, Iss. 19, pp. 12340-12340
Open Access | Times Cited: 18
Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
Ibrahim Aldhari, Meshal Almoshaogeh, Arshad Jamal, et al.
Applied Sciences (2022) Vol. 13, Iss. 1, pp. 233-233
Open Access | Times Cited: 18
Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind tunnel study
Afaq Khattak, Pak Wai Chan, Feng Chen, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 10
Analysis of traffic accident causes based on data augmentation and ensemble learning with high-dimensional small-sample data
Leipeng Zhu, Zhiqing Zhang, Dongdong Song, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121782-121782
Closed Access | Times Cited: 10
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