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.
翻译:众所周知,交通排放会大大加剧世界各地的空气污染,特别是在新加坡等城市化程度较高的城市,过去曾表明,主要公路沿线的微粒污染与高峰时段交通量的增加有着密切的关联,交通排放量的减少可导致更好的健康结果,然而,在许多情况下,获得适当的车辆交通量仍然人工统计,而且极为艰苦,从而限制了人们长时间进行纵向监测的能力,例如,当人们试图了解新的交通条例(例如汽车合用)或计算模型等干预措施的效力时,则会发现,在本研究中,我们提议并采用一个综合机器学习管道,利用交通图象取得交通量的计算结果,很容易与其他测量结果相结合,以便利各种研究。我们核实该管道在新加坡一个地点获得的交通图像公开源数据集上的效用和准确性,并将所获得的车辆量与2022年2周内获得的相交点颗粒测量数据进行比较。观察到,路边颗粒排放与获得的温度计确实与获得的气压计值密切相关,可以表明这一方法作为快速的温度系数。