在十字路口的碰撞率分析中整合视觉因素:对自行车安全的AutoML和SHAP方法
Huiyuan Xue1, Peizhuo Guo1, Yiyan Li2
1Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China.
Accident; analysis and prevention
|March 17, 2024
概括
本研究使用先进的机器学习确定了导致城市自行车撞车事故的关键视觉因素. 调查结果强调道路障碍,开放的天空和可见的卡车作为重大风险,为更安全的城市规划提供信息.
科学领域:
- 城市规划是城市规划.
- 交通运输安全运输安全
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 自行车撞车在城市交通中越来越令人担忧.
- 现有的研究往往忽略了视觉环境因素和风险的空间变化.
- 之前模型的有限解释性阻碍了有效的安全干预.
研究的目的:
- 制定一个框架来确定城市级自行车安全因素,强调视觉元素.
- 分析这些因素对骑自行车撞车风险的贡献的空间变化.
- 提供对自行车事故的主要原因的可解释的见解.
主要方法:
- 利用基于原点和目的地的自行车流和谷歌街景图像来提取视觉因素.
- 使用比较自动机器学习 (AutoML) 进行模型选择.
- 应用基于价值的SHAP (夏普利添加式解释) 地理空间分析,用于因子解释性和空间变化评估.
主要成果:
- 轻GBM模型在预测自行车撞车率方面表现出了卓越的表现.
- 确定的主要视觉风险因素包括道路障碍物,开放天空和可见卡车的比例.
- SHAP分析揭示了这些视觉因素影响的显著空间变化.
结论:
- 拟议的框架有效地识别和解释城市自行车环境中的视觉风险因素.
- 特定的视觉元素,如道路障碍物和可见的卡车,是骑自行车安全的关键决定因素.
- 在确定的高风险地区进行有针对性的干预,可以提高像曼哈顿这样的城市的自行车安全.
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