一个全面的多目标框架,用于估计碰撞频率模型的估计
Zeke Ahern1, Paul Corry2, Mohammadali Shirazi3
1School of Civil & Environment Engineering, Queensland University of Technology, 2 George Street, Brisbane, 4000 QLD, Australia.
Accident; analysis and prevention
|December 3, 2024
概括
这项研究引入了一种新的崩数据分析框架,优化模型以处理未观察到的异质性并提高准确性. 该MetaCountRegressor Python包提供了一个系统的方法,用于更好的崩频率建模.
科学领域:
- 交通安全与工程
- 统计建模 统计建模
- 运输科学 运输科学
背景情况:
- 未观察到的异质性是崩频率分析的一个重大挑战,通常用随机参数和专业分布来解决.
- 现有的方法需要对功能形式,转换和贡献因素进行广泛的假设测试,风险偏差和次优模型.
- 同时考虑多个目标,如适合性和各种异质性方面,使模型开发复杂化.
研究的目的:
- 提出一个全面的优化框架,用于系统的假设测试在崩数据建模.
- 为了应对未观察到的异质性的挑战,同时分组随机参数,功能形式和贡献因素识别.
- 减少与碰撞频率分析中有限测试相关的偏差和成本.
主要方法:
- 开发了一个数学编程公式,用于全面的优化框架.
- 使用元启发式算法 (模拟化,差异进化,和搜索) 进行复杂的估计和优化.
- 通过使用三个真实世界的崩数据集验证了框架.
主要成果:
- 拟议的框架有效地估计了健全和节的撞车数据计数模型.
- 和搜索证明了强大的收与低超参数灵敏度.
- 结果是健全和一致的,表现优于已公布的模型,并降低了开发成本.
结论:
- 优化框架系统地解决了崩分析中的多个建模决策和目标.
- 该MetaCountRegressor Python包为研究人员和从业人员提供了一个有价值的工具.
- 这种方法导致更可靠,可转移和更具成本效益的碰撞频率模型.
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