向高速公路事故严重程度的高效准确预测使用两级模糊综合评估
Guanghui Wang1,2, Jinbo Li1, Lingfeng Shen1
1School of Software, Henan University, Kaifeng, 475004, China.
Heliyon
|September 12, 2024
概括
本研究介绍了一种使用双层模糊综合评估的高速公路事故严重性预测 (FASP) 方法. FASP准确地预测了事故严重程度,提高了道路安全和应急响应,计算成本低.
科学领域:
- 智能运输系统 智能运输系统
- 道路安全工程工程 道路安全工程
- 模糊逻辑应用程序模糊逻辑应用程序
背景情况:
- 准确的高速公路事故严重程度预测对于有效的道路安全和应急响应至关重要.
- 现有的方法缺乏智能高速公路系统所需的精度.
- 目前的限制阻碍了及时有效的高速公路救援行动.
研究的目的:
- 开发一种高效准确的高速公路事故严重性预测 (FASP) 方法.
- 将影响因素分为人类 (6类) 和非人类 (36类) 的类别.
- 为了提高高速公路事故严重程度预测的精度.
主要方法:
- 将影响因素分为两个层次:人类和非人类.
- 开发使用双层模糊综合评估的高速公路事故严重性预测 (FASP) 方法.
- 使用模糊的评估矩阵,用于重量计算的法和用于预测的最大成员原则.
主要成果:
- FASP方法在预测高速公路事故严重程度方面表现出很高的准确性.
- 来自中国和美国的数据集的实验验证实了该方法的有效性.
- 该方法提供了对具有较低计算成本的因素的彻底考虑.
结论:
- FASP为高速公路事故严重程度预测提供了准确有效的解决方案.
- 这种方法代表了两个层次的模糊综合评估在这个领域的新应用.
- 这些发现有助于改进智能高速公路系统和应急管理.
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