{
  "abstract": "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–temporal 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.",
  "abstract_shown": true,
  "affiliations": [
    "Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India",
    "School of Computer Science, Faculty of Science and Engineering, University of Nottingham, Ningbo China, Ningbo, Zhejiang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sanchit Vashisht"
    },
    {
      "affs": [
        1
      ],
      "name": "Shalli Rani"
    },
    {
      "name": "Fazlullah Khan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/60zkjn9tjv6pr.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357089",
  "fulltext_url": null,
  "id": "60zkjn9tjv6pr",
  "issns": [
    "1932-6203"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2213|Traffic and Road Safety"
    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Intelligence"
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      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Intelligence"
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      "plos",
      "/Engineering and technology/Civil engineering/Transportation infrastructure/Roads"
    ],
    [
      "plos",
      "/Engineering and technology/Transportation"
    ],
    [
      "plos",
      "/Engineering and technology/Transportation/Transportation infrastructure/Roads"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors/Traumatic injury risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors/Traumatic injury risk factors/Road traffic collisions"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Safety/Traffic safety"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Traumatic injury risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Traumatic injury risk factors/Road traffic collisions"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Intelligence"
    ]
  ],
  "openalex": "W7220856089",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357089&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/60zkjn9tjv6pr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0357089",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357089"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "TRIDENT-Risk: An AI-driven temporal ensemble framework for proactive high-severity accident risk forecasting in Intelligent Transportation Systems",
  "title_text": "TRIDENT-Risk: An AI-driven temporal ensemble framework for proactive high-severity accident risk forecasting in Intelligent Transportation Systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/60zkjn9tjv6pr",
  "venue": "PLOS One",
  "versions": []
}