Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques
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- Seunghoon Kim & Youngbin Lym & Ki-Jung Kim, 2021. "Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly Drivers," IJERPH, MDPI, vol. 18(4), pages 1-23, February.
- Manze Guo & Zhenzhou Yuan & Bruce Janson & Yongxin Peng & Yang Yang & Wencheng Wang, 2021. "Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost," Sustainability, MDPI, vol. 13(2), pages 1-26, January.
- Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
- Neda Abdelhamid & Arun Padmavathy & David Peebles & Fadi Thabtah & Daymond Goulder-Horobin, 2020. "Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.
- Mubarak Alrumaidhi & Hesham A. Rakha, 2022. "Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
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Keywords
crash severity; machine learning; resampling techniques; imbalance data; road safety; elderly drivers; transportation safety;All these keywords.
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