Road Emissions in London: Insights from Geographically Detailed Classification and Regression Modelling

Abstract

Greenhouse gases and air pollutant emissions originating from road transport continues to rise in the UK, indicating a significant contribution to climate change and negative impacts on human health and ecosystems. However, emissions are usually estimated at aggregated levels, and on many occasions roads of minor importance are not taken into account, normally due to lack of traffic counts. This paper presents a methodology enabling estimation of air pollutants and CO2 for each street segment in the Greater London area. This is achieved by applying a hybrid probabilistic classification–regression approach on a set of variables believed to affect traffic volumes and utilizing emission factors. The output reveals pollution hot spots and the effects of open spaces in a spatially rich dataset. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels.

Keywords

• greenhouse gases
• air pollution
• gradient boosting machine
• GBM
• probabilistic classification
• annual average daily traffic (AADT)