PL-TARMI:一个深度学习框架,用于像素级交通事故风险地图推断.
Qiuyang Huang1, Hongfei Jia1, Zhilu Yuan2
1College of Transportation, Jilin University, Changchun, 130012, China.
Accident; analysis and prevention
|July 7, 2023
概括
本研究介绍了PL-TARMI,这是一个深度学习框架,用于创建详细的全市交通事故风险地图. 它使用可访问的数据以提高交通安全指导的成本效益.
科学领域:
- 城市规划是城市规划.
- 地理信息系统 (GIS) 是指地理信息系统.
- 运输工程 运输工程 运输工程
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 准确的全市交通事故风险测绘对于预防事故至关重要.
- 由于复杂的道路网络,人类行为和广泛的数据需求,细粒度的地理交通事故风险推断具有挑战性.
研究的目的:
- 开发一个深度学习框架,PL-TARMI,用于准确,细粒度的交通事故风险地图推断.
- 利用易于获取的数据进行成本效益高的交通安全分析和预防指导.
主要方法:
- 整合卫星图像和道路网络数据.
- 与可访问的数据源相结合,包括兴趣点 (POI) 分布,人类流动性和交通数据.
- 开发一个深度学习模型 (PL-TARMI) 用于像素级风险地图生成.
主要成果:
- PL-TARMI成功地生成了像素级交通事故风险地图.
- 该框架有效地利用多样化,可访问的数据输入.
- 在真实世界数据集上的实验结果验证了模型的有效性.
结论:
- PL-TARMI提供了一种新且有效的方法,用于细粒度的交通事故风险评估.
- 该框架提供了更合理,更具成本效益的交通事故预防指导.
- 这种方法解决了复杂的道路网络和风险映射中的数据要求的挑战.
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