{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T18:00:35Z","timestamp":1791396035669,"version":"4.3.3"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100021178","name":"University of Nottingham Ningbo China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021178","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>High-severity traffic accidents impose substantial human and economic burdens, necessitating proactive and data-driven risk forecasting mechanisms within intelligent transportation systems. This study proposed TRIDENT-Risk, a temporal risk-driven imbalance-optimized ensemble framework for high-severity accident prediction using the eXtreme Gradient Boosting (XGBoost) model. The approach integrates spatial\u2013temporal aggregation, multi-scale lag and rolling risk features, imbalance-aware gradient boosting, and threshold optimization to enhance minority-class detection. Experimental evaluation using publicly accessible US-Accidents dataset including about 7.7 million accident records from February 2016 to March 2023 demonstrated that the proposed TRIDENT-Risk achieved a ROC-AUC of 0.8917, PR-AUC of 0.7266, precision of 0.60, recall of 0.81, F1-score of 0.69 and accuracy of 0.82. Compared with the strongest baseline (MLP), the proposed framework improved high-risk recall from 0.70 to 0.81 (15.7% relative improvement), increased ROC-AUC from 0.8776 to 0.8917 and improved PR-AUC from 0.7016 to 0.7266, demonstrating superior minority-class detection and reliable proactive accident forecasting. Temporal grid-based aggregation was used to frame high-severity accidents as a binary prediction problem and an 80:20 chronological train-test split was used to eliminate temporal leaking. The proposed framework offers a scalable and interpretable solution for proactive traffic safety management and supports intelligent decision-making in next-generation transportation systems.<\/jats:p>","DOI":"10.1371\/journal.pone.0357089","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:41:23Z","timestamp":1791394883000},"page":"e0357089","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["TRIDENT-Risk: An AI-driven temporal ensemble framework for proactive high-severity accident risk forecasting in Intelligent Transportation 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