道路輸送車両の危険性に関する知識グラフに基づく安全リスク評価方法
1College of Automotive and Traffic Engineering, Nanjing Forestry University, Nanjing, China.
Traffic injury prevention
|August 20, 2025
まとめ
この研究は,危険な運転行動と交通安全を分析するための知識グラフ (KG) フレームワークを導入します. 危険な運転とドライバーの能力は重要なリスク因子であり,KG経路分析は事故の原因メカニズムを明らかにします.
科学分野:
- 交通安全に関する研究
- 輸送工学
- データサイエンス
背景:
- 危険な運転は,道路交通の安全性に大きな影響を与える.
- ドライバーの行動と事故の複雑な関係を定量化することは,予防に不可欠です.
研究 の 目的:
- 危険な運転行動が交通安全に与える影響を分析するための知識グラフ (KG) ベースの枠組みを開発する.
- 交通事故の主要な危険因子と因果連鎖を定量化し,特定する.
主な方法:
- 7つのカテゴリーからなる KG を構築した
- マルチソースの事故データを統合して 関連性をマップする.
- クリテリア間相関による基準の重要度 (CRITIC) とコサイン相似度分析を適用した.
- 原因連鎖を追跡するためにKGベースの経路推論を使用しました.
主要な成果:
- 危険な運転 (C5) とドライバーの技術的な能力 (C1) は,最も影響のあるリスク要因として特定されました.
- 危険な運転に関連した事故の種類としては,後方衝突事故が最も多い.
- 疲労と速度の上昇は一般的な行動誘発因子で,KG分析はリスク伝播経路を効果的に追跡した.
結論:
- この研究は,KG分析とCRITICメソッドを統合して,交通事故の行動メカニズムを包括的に理解します.
- 開発されたフレームワークは,危険な行動から事故の種類と原因までの因果連鎖を明示的に視覚化しています.
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