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.

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

“…It was also found that 80% of air pollution in the Lima Metropolitan Area was produced from automobiles [13]. in China, 85% of emissions were from transport [15], while in the United Kingdom was 92% [16], and 75% in Malaysia [17], but in the United States, automobiles are responsible for emitting 57% of air pollution [7]. Many other studies also show that 92% of CO and 65% of hydrocarbon (HC) pollutants were emitted from the transportation activities in Shanghai [18], and 60% of N O x and P M emissions were from heavy-duty trucks in China [19].…”

Factors Affecting Road Traffic: Identifying Drivers of Annual Average Daily Traffic Using Least Absolute Shrinkage and Selection Operator Regression

“…Consequently, several methods to estimate traffic volumes across the network have been investigated, taking into consideration, to a different extent, the factors associated with traffic. In particular, annual average daily traffic (AADT)-a measure of traffic volume (traffic volume definition in provided in the Supplementary Information) defined as the average daily traffic on a street segment over a year (4)-has been the research focus for studies on motorized (5) and non-motorized transport (6,7), aiming to address issues such as accident prediction (8) and air pollutant estimation (9). Whilst research on AADT estimation and related driving factors has been improved with the incorporation of novel modeling approaches, significant limitations are still observed.…”