Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline

TL;DR

This paper introduces an automated image analysis pipeline using machine learning to count vehicles from traffic images, enabling easier correlation with particulate emissions and supporting air quality studies.

Contribution

The study presents a novel automated pipeline for vehicular counting from traffic images, validated against particulate measurements, facilitating long-term traffic and pollution monitoring.

Findings

Vehicular counts strongly correlate with particulate emissions (correlation coefficient 0.93).

The pipeline enables quick, automated traffic quantification from images.

Validated on Singapore traffic data over two weeks.

Abstract

Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore. It has been previously shown that the particulate pollution along major roadways exhibit strong correlation with increased traffic during peak hours, and that reductions in traffic emissions can lead to better health outcomes. However, in many instances, obtaining proper counts of vehicular traffic remains manual and extremely laborious. This then restricts one's ability to carry out longitudinal monitoring for extended periods, for example, when trying to understand the efficacy of intervention measures such as new traffic regulations (e.g. car-pooling) or for computational modelling. Hence, in this study, we propose and implement an integrated machine learning pipeline that utilizes traffic images to obtain vehicular counts that can be easily integrated with other measurements to facilitate various studies. We verify the utility and accuracy of this pipeline on an open-source dataset of traffic images obtained for a location in Singapore and compare the obtained vehicular counts with collocated particulate measurement data obtained over a 2-week period in 2022. The roadside particulate emission is observed to correlate well with obtained vehicular counts with a correlation coefficient of 0.93, indicating that this method can indeed serve as a quick and effective correlate of particulate emissions.