一个混合决策支持系统,专注于通过整合iRAP和模糊的AHP方法进行深入的道路风险评估
Fatma Zohra Gherbi1, Ramdane Oulha2, Mohamed Amine Hamadouche1
1Laboratory of Research on Geomatic, Ecology and Environment, University of Mustapha Stambouli, Mascara, Algeria.
Traffic injury prevention
|February 4, 2026
概括
这项研究开发了一种混合道路风险模型,将基础设施,交通和人为因素整合起来. 该模型准确地识别了高风险的道路段,改进了超越传统方法的安全评估.
科学领域:
- 道路安全工程工程 道路安全工程
- 运输风险评估 运输风险评估
- 交通管理系统是交通管理系统.
背景情况:
- 交通事故是基础设施,交通,驾驶员行为和环境之间的复杂相互作用造成的.
- 基于几何学的传统方法在反映现实世界的道路网络复杂性方面存在局限性.
- 开发适应性决策支持工具对于有效的道路安全管理至关重要.
研究的目的:
- 开发一个综合分析框架,用于评估和建模道路风险.
- 识别高风险路段并量化它们的风险水平.
- 优先考虑在不同地区加强道路安全的干预措施.
主要方法:
- 这是一种混合方法,结合了国际道路评估计划 (iRAP) 和模糊分析层次过程 (Fuzzy AHP).
- 分析了阿尔及利亚多个地区的999个路段 (每段100米).
- 开发一个简化的道路风险评估模型,包括安全因素及其权重.
主要成果:
- 基础设施因素显著影响道路风险,其次是人类和交通变量;环境因素的影响最小.
- 混合模型通过整合行为和动态参数,显示出比iRAP更好的准确性.
- 该模型的风险水平与实际事故数据之间的强烈相关性证实了其预测可靠性.
结论:
- 混合方法提供了一个可靠和可适应的工具,用于全面的道路安全评估.
- 该模型通过整合多种影响因素,提供了对道路风险的现实观点.
- 研究结果支持针对性的安全干预措施,如车道分隔,改善可见性和速度管理,特别是在数据有限的地区.
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