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.