Integration of Traffic Management and an Artificial Intelligence to Evaluate Urban Air Quality

Abstract

Emissions from motor vehicles are the primary source of air pollution, especially in congested urban centres. However, through effective traffic management, it has been found that the level of pollution can be significantly reduced, facilitating the mobility of urban arterials. This study aims to quantify the extent of traffic emissions and to identify the influence of traffic management to improve air quality and reducing traffic emissions. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to estimate the extent of traffic emissions (N02 and PM10) at certain intersections. Then, a traffic management simulation software was also used to simulate traffic and to build a traffic improvement scenario at these intersections. This was followed by measuring the improvement in air quality due to traffic management modification, analysed using the developed ANFIS model. The results showed that reducing the delay at certain intersections may reduce N02 and PM10 significantly. The proposed hybrid model increased the forecasting accuracy and improved the perception between the relationship between traffic characteristics and pollutant emissions. Additionally, it facilitates the work of city planners and helps decision making regarding urban air quality.