1 research outputs found
Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline
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