{
  "abstract": "Traffic accidents arise from the dynamic evolution of traffic flow under spatio-temporal effects, and their occurrence and severity are significantly influenced by temporal and spatial variations in traffic conditions. Traditional studies predominantly rely on static accident-time data, which limits prediction accuracy. To address this gap, this study proposes a deep learning framework that integrates data preprocessing, spatio-temporal effect analysis, PCA-FCM-based risk labeling, and a CNN-BiLSTM hybrid model. Compared with CNN-LSTM, the proposed model replaces unidirectional LSTM with bidirectional LSTM to capture both forward and backward temporal dependencies. Based on the HighD naturalistic trajectory dataset, the CNN-BiLSTM model achieves an MSE of 0.362, RMSE of 0.601, MAE of 0.197 and MAPE of 0.044, outperforming LSTM, BiLSTM and CNN-LSTM with moderate running time. In accident risk classification, it reaches an overall accuracy of 90%, with class-level precision of 0.90–0.93 and F1 scores of 0.82–0.96. The results demonstrate that the proposed method effectively captures spatio-temporal dynamic characteristics and provides an efficient tool for expressway accident risk prediction.",
  "abstract_shown": true,
  "affiliations": [
    "School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan, China.",
    "Shandong Zhengqu Traffic Engineering Co., Ltd, Jinan, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Liang Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Duo Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Han Bai"
    },
    {
      "affs": [
        0
      ],
      "name": "Tianzheng Wei",
      "orcid": "0000-0002-2682-4054"
    },
    {
      "affs": [
        0
      ],
      "name": "Jian Lu"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiuguang Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/csafgktk1g795.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360312",
  "fulltext_url": "https://europepmc.org/articles/PMC13693914",
  "id": "csafgktk1g795",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360312",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "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",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
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      "plos",
      "/Engineering and technology/Civil engineering/Transportation infrastructure/Roads"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "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/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/Road traffic collisions"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Acceleration"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ]
  ],
  "openalex": "W7223288725",
  "pdf_url": "https://europepmc.org/articles/PMC13693914?pdf=render",
  "pmcid": "PMC13693914",
  "pmid": "42912816",
  "published": "2026-10-23",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/csafgktk1g795.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pone.0360312",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360312"
    },
    {
      "first_seen": "2026-10-30T12:20:03Z",
      "native_id": "MED:42912816",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42912816"
    }
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  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Research on expressway traffic accident risk prediction method considering Spatio-Temporal effects",
  "title_text": "Research on expressway traffic accident risk prediction method considering Spatio-Temporal effects",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:03Z",
  "url": "https://research.draconitic.com/paper/csafgktk1g795",
  "venue": "PLOS One",
  "versions": []
}