Abstract Summary
The pressing challenge of mitigating climate change demands precise measurements of urban emissions. Traffic, a significant contributor, necessitates accurate quantification for effective reduction. However, cities globally struggle due to the complex spatial and temporal emission variations. This study introduces a novel approach employing computer vision to map vehicle emissions at the make and model level on urban road networks. Our methodology utilizes a unique car model classification dataset, DRIVE, comprising 2.2 million images, enabling the classification of 4,923 car models. In addition, we associate each model with its emission standard using the COPERT model. We enhance speed estimation and emission accuracy by leveraging vehicle tracking and distance measurement from video footage. The application of our method in Amsterdam demonstrates the potential for real-time traffic flow detection and emission estimation worldwide. Our findings contribute to addressing emissions measurement challenges in dense urban areas.
