多个规模的方法来应对宏观崩分析中的规模效应问题
Xiaoqi Zhai1, N N Sze2, Jaeyoung Jay Lee3
1School of Civil Engineering, Zhengzhou University, Zhengzhou, Zhengzhou, China.
Accident; analysis and prevention
|February 23, 2025
概括
交通安全分析受到空间尺度的影响. 新的贝叶斯多尺度模型有效地解决了崩预测中的规模效应,提高了聚合地理数据的准确性.
科学领域:
- 运输工程 运输工程
- 空间统计的空间统计.
- 交通安全分析 交通安全分析
背景情况:
- 交通安全对于运输规划和基础设施管理至关重要.
- 在交通分析中的地理数据聚合容易出现可修改面积单位问题 (MAUP).
- 高级空间聚合可能导致规模效应,丢失详细信息并影响分析结果.
研究的目的:
- 为交通事故分析提出和评估贝叶斯的多尺度模型.
- 为了解决交通和事故数据的空间聚合造成的规模效应.
- 提高不同地理规模的撞击预测模型的性能.
主要方法:
- 开发贝叶斯的多尺度模型来解释空间尺度的变化.
- 拟议模型与传统的独立模型进行比较.
- 利用了来自佛罗里达州希尔斯伯勒县的两种规模的机数据:区块组和人口普查区.
主要成果:
- 提出的贝叶斯多尺度模型有效考虑了撞车数据中的规模效应.
- 与传统模型相比,多尺度模型表现出更好的性能,特别是在更高的聚合水平上.
- 在人口普查管道层面分析中观察到模型性能显著改善.
结论:
- 贝叶斯的多尺度模型为宏观撞击分析中的尺度效应提供了强大的解决方案.
- 这些模型使用聚合的空间数据提高了交通安全预测的可靠性.
- 这些发现对道路基础设施设计和运输规划具有实际意义.
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