地理加权机器学习用于模拟美国交通事故频率和决定因素的空间异质性
Shuli Wang1, Kun Gao2, Lanfang Zhang3
1Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, CN-201804, China; Department of Architecture and Civil Engineering, Chalmers University of Technology, Goteburg SE-412 96, Sweden.
Accident; analysis and prevention
|March 6, 2024
概括
地理加权随机森林 (GW-RF) 模拟了交通事故因素,揭示了它们影响的空间变化. 交叉口密度等道路网络特征是关键,有助于针对性的安全干预.
科学领域:
- 运输科学 运输科学
- 空间分析 空间分析
- 机器学习在地球科学中的应用
背景情况:
- 交通事故数据显示出显著的空间依赖性和异质性.
- 了解影响事故因素的局部变化对于有效的道路安全至关重要.
- 以前的全球模型往往无法捕捉这些空间细微差别.
研究的目的:
- 使用数据驱动的方法,探索交通事故频率和影响因素之间的局部关联.
- 模拟和理解空间异质性因素对不同地理区域的碰撞频率的影响.
- 将地理加权随机森林 (GW-RF) 的性能与全球模型和传统的地理加权回归进行比较.
主要方法:
- 地理加权随机森林 (GW-RF) 模型的应用.
- 多种数据源的整合:道路网络属性,社会经济特征和土地使用因素.
- 分析不同因素对碰撞频率的影响的空间变化.
主要成果:
- 在全球随机森林和地理加权回归相比,GW-RF模型显示出更高的预测准确度.
- 在因素的影响中发现了显著的空间变化;交叉密度在美国南部和东北部非常重要.
- 低级道路密度显示在特定城市具有局部影响,而道路网络因素具有普遍重要意义.
结论:
- 该GW-RF模型有效地捕捉了交通事故影响因素的空间异质性.
- 道路网络属性,特别是交叉口密度,始终是事故发生频率的重要预测因素.
- 调查结果支持资源分配和地理定制干预措施的发展,以提高道路安全.
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