从预测到预防:使用文本挖掘和可解释的机器学习进行城市公共汽车事故分析
Bowei Chen1, Yufei Huang2, Yu Zheng3
1Adam Smith Business School, University of Glasgow, Glasgow, UK.
概括
这项研究使用机器学习来分析城市公共汽车事故,识别滑板车碰撞和突然停止等关键风险因素. 这些发现为改善运输安全提供了可操作的见解.
科学领域:
- 运输安全运输安全
- 数据科学数据科学数据科学
- 风险分析 风险分析
背景情况:
- 城市公共汽车事故在人口密地区带来了重大的安全和运营挑战.
- 事故数据往往包含非结构化的叙事信息,对于了解原因至关重要.
- 现有的分析方法可能缺乏有效安全干预所需的解释性.
研究的目的:
- 开发和验证机器学习框架,用于识别,量化和解释导致严重公共汽车事故的因素.
- 通过整合非结构化文本数据,提高事故严重程度模型的预测准确性和可解释性.
- 为利益相关者提供可操作的见解,以实施有针对性的安全措施.
主要方法:
- 整合一个结构主题模型 (STM) 来从叙事数据中提取事故场景.
- 使用极端梯度提升 (XGBoost) 分类器来预测事故严重程度.
- 应用夏普利添加式解释 (SHAP) 用于模型预测的全球和本地解释.
主要成果:
- 机器学习框架通过结合文本衍生事故模式,显著提高了预测准确性和可解释性.
- 确定的主要风险因素包括与电动滑板车的后方碰撞,导致乘客受伤的突然停止,以及交通拥堵中左转的机动.
- SHAP分析提供了明确的,可操作的洞察力,在整体和特定事件层面上对事故造成的原因.
结论:
- 开发的分析框架有效地整合了结构化和非结构化数据,以进行可解释的运输风险建模.
- 调查结果为司机,运输运营商和决策者提供了切实可行的指导,以减轻城市公共汽车事故风险.
- 模块化方法为各种运输和安全领域的风险分析提供了可转移的基础.
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