确定与摩托车人撞车率相关的环境因素:使用空间随机森林与网络距离和障碍物的变量选择
Bimo Harya Tedjo1, Pei-Fen Kuo1, Febrian Fitryanik Susanta2
1Department of Geomatics, National Cheng Kung University, Taiwan.
Accident; analysis and prevention
|December 20, 2025
概括
摩托车事故是全球安全问题. 一个新的框架,空间RF-NDBAR,通过考虑道路网络和障碍物,识别在感兴趣点附近的高风险区域来改善碰撞预测.
科学领域:
- 运输安全运输安全
- 空间分析 空间分析
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 摩托车事故对全球安全构成重大风险,特别是在亚洲国家.
- 分析碰撞因素的传统方法往往忽略了空间模式和道路网络限制,限制了准确性.
- 环境因素,比如对某些道路的限制,对于了解摩托车撞车事件至关重要.
研究的目的:
- 引入一个新的空间变量选择框架,带有网络距离和障碍的空间随机森林 (空间RF-NDBAR).
- 通过结合网络距离和道路障碍信息,提高摩托车碰撞预测模型的准确性.
- 确定与城市环境中摩托车撞车风险增加相关的关键因素和位置.
主要方法:
- 开发并应用空间RF-NDBAR框架用于摩托车碰撞分析中的变量选择.
- 使用的摩托车事故数据 (2016-2020年) 和 OpenStreetMap 台北市的兴趣点 (POI) 数据.
- 将空间RF-NDBAR与传统的空间RF和空间RF-ND方法进行比较,使用普通最小平方和多尺度地理加权回归 (MGWR) 模型评估性能.
主要成果:
- 使用空间RF-NDBAR选择的变量,MGWR模型表现出最高的性能,具有最低的AICc (1801.212) 和最高的R2 (0.867).
- 整合空间预测器有效地减少了剩余的空间自相关性,从而提高了模型的准确性.
- 特定的POI,包括市场,餐厅,酒吧,酒店,银行,巴士站和地铁站,与更高的摩托车撞车风险有显著联系.
结论:
- 空间RF-NDBAR框架在碰撞分析中为空间变量选择提供了显著的改进.
- 了解和结合空间约束和网络距离对于准确的摩托车碰撞建模至关重要.
- 调查结果支持针对已识别的高风险POI的交通安全干预措施,以减轻摩托车事故.
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