Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment
The results indicate that traffic-related features significantly contribute to TRAP and provide insights and guidance for urban planning. By incorporating crowd-sourced Google traffic information, we assessed traffic abatement scenarios that could inform targeted strategies for improving urban air quality.
Authors
- Peng Wei
- Zhi Ning (ORCID: https://orcid.org/0000-0002-8223-1501)
- Mengyuan Chu (ORCID: https://orcid.org/0000-0002-4406-0027)
- Yuan Shi (ORCID: https://orcid.org/0000-0003-4011-8735)
- Hao Song (ORCID: https://orcid.org/0000-0003-0876-485X)
- Wang Ya (ORCID: https://orcid.org/0000-0002-5217-282X)
- Abhishek Anand (ORCID: https://orcid.org/0000-0001-8263-192X)
Institutions
- University of Liverpool (GB)
- Hong Kong University of Science and Technology (HK)
- Shandong Normal University (CN)
- State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
- Carnegie Mellon University (US)
- University of Hong Kong (HK)
Publication Details
- Journal
- Environment International
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1016/j.envint.2024.108992
- Citations
- 13
- Primary Topic
- Air Quality and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 4.09