通过使用潜在类集群和关联规则,识别了与公共汽车事故有关的道路设施的贡献链和模式
Chunting Nie1,2, Shunchao Wang3, Qinghai Lin4
1College of Transportation, Tongji University, Shanghai, P.R. China.
Traffic injury prevention
|January 20, 2026
概括
公共汽车撞车模式因严重程度和集群而异. 道路基础设施,如双通道和十字路口,对事故类型和结果有独特的贡献,为目标安全干预提供了信息.
科学领域:
- 运输安全运输安全
- 数据挖掘 数据挖掘
- 城市规划 城市规划
背景情况:
- 涉及公共汽车的事故带来了复杂的安全挑战.
- 了解碰撞异质性对于有效的干预至关重要.
- 现有的分析往往缺乏关于道路基础设施作用的细节性.
研究的目的:
- 通过使用协会规则和道路设施模式,调查公共汽车事故的异质性.
- 为了识别不同类型和严重程度的事故中导致的因素.
- 支持城市公交系统的有针对性和特定环境的安全干预措施.
主要方法:
- 在伦敦 (2010-2019) 分析了14560起公共汽车事故.
- 应用隐性类型集群 (LCC) 以无法控制的因素对撞车事件进行分组.
- 利用协会规则挖矿 (ARM) 来识别集群和严重程度的道路基础设施模式.
主要成果:
- 碰撞模式和促成因素在各个集群和严重程度上有很大差异.
- 双通道和白天光线与轻微和严重的碰撞有关.
- 特定的交叉点类型和交叉点对于某些集群中的轻微碰撞至关重要.
结论:
- 集成的LCC和ARM框架为公共汽车事故的原因提供了细微的见解.
- 对道路基础设施对事故严重程度和类型的贡献产生可操作的情报.
- 这些发现为目标安全措施和改善城市公共汽车安全基础设施提供了信息.
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