将谷歌交通图与深度学习模型结合起来,在复杂的城市环境中预测街道交通相关的空气污染物
Peng Wei1, Song Hao2, Yuan Shi3
1College of Geography and Environment, Shandong Normal University, Jinan, China; Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
Environment international
|September 9, 2024
概括
这项研究开发了一种深度学习模型,准确估计香港交通相关空气污染 (TRAP) 的细度. 该模型通过实时交通数据增强,显著改善了对氧化 (NOx) 度的预测.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 交通相关空气污染 (TRAP) 为城市空气质量建模带来了重大挑战,原因是街道水平的急剧变化.
- 传统模型很难在复杂的城市环境中捕捉微小尺度的TRAP.
研究的目的:
- 为了估计香港的氧化 (NO和NO2) 的细度 (50m) 度.
- 开发和评估用于TRAP评估的深度学习 (DL) 模型.
主要方法:
- 利用公共汽车上的移动空气质量传感器和众筹的谷歌交通数据.
- 开发了一个DL模型,并将其性能与现有的机器学习模型进行比较.
- 使用可解释的机器学习来评估特征效应.
主要成果:
- DL模型实现了高精度,NO的R2值为0.72,NO2.0的R2值为0.69.
- 结合实时流量状态,模型性能提高了9%至17%.
- 确定了与交通相关的特征及其相互作用作为关键预测因素.
结论:
- 与交通相关的特征是TRAP的重要贡献者.
- 众筹的交通数据和DL模型为改善城市空气质量提供了有效的策略.
- 调查结果为城市规划和有针对性的污染减排提供了指导.
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