走向更安全的街道:使用街景图像揭示行人感知道路安全的框架
Omar Faruqe Hamim1, Satish V Ukkusuri2
1Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA; Department of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.
Accident; analysis and prevention
|November 29, 2023
概括
这项研究开发了一个可扩展的框架,以使用谷歌街景图像和深度学习来评估行人道路安全,这对于事故数据有限的地区至关重要. 调查结果突出了基础设施和社会经济因素对感知安全的影响,有助于针对性干预.
科学领域:
- 城市规划和交通安全.
- 计算机视觉和深度学习应用程序
- 地理空间分析和公共卫生
背景情况:
- 全球道路安全是一个主要问题,特别是在低收入和中等收入国家,事故数据往往不完整.
- 在许多地区,道路交通事故和近乎事故的报告不足限制了有效的安全分析和干预.
- 评估道路安全感知对于了解行人脆弱性和告知政策至关重要.
研究的目的:
- 提出一个可扩展的框架来评估行人的道路安全感知,特别是在数据稀缺的环境中.
- 确定影响行人道路安全感知的关键环境和社会经济因素.
- 为城市规划和干预创建一个社区规模的道路安全地图.
主要方法:
- 在谷歌街景 (GSV) 图像上训练了一个深度学习语义细分模型 (HRNet+OCR),以识别道路特征.
- 经过培训的评级人员从GSV图像中评估了道路安全 (0-10级) 的感知.
- 随机森林回归,结合GSV特征和社会人口统计数据,估计感知安全性;SHAP值确定特征的重要性.
主要成果:
- 语义细分模型在识别城市特征方面取得了很高的准确性 (例如,F1得分为0.96).
- 人行道,道路基础设施,人口密度,墙壁和相对财富指数显著影响了道路安全评级的感知.
- 在易发生碰撞的地区和不易发生碰撞的地区之间发现了感知安全得分的显著差异.
结论:
- 开发的框架有效地使用GSV图像和机器学习来估计行人感知到的道路安全,即使事故数据有限.
- 该研究提供了对影响行人安全感知的因素的可操作的见解,这对于有针对性的城市规划至关重要.
- 社区规模的道路安全地图是政策制定者识别和解决安全缺陷的宝贵工具.
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