Traffic data for local emissions monitoring at a signalized intersection

In order to assist planning efforts for air pollution-responsive dynamic traffic management (DTM) systems, this research assesses the accuracy of local emissions monitoring based on traffic data and models. The study quantifies the benefits of increased data resolution for short-term emissions estimates at a signalized intersection. The emissions estimates are also compared with air quality measurements in the immediate roadside environment. Results show that traffic-based emissions estimates require detailed knowledge of the local vehicle fleet and speed profiles. Traffic-based emissions monitoring enables pollution-responsive DTM, but these results indicate that this approach only applies over long time periods. This limit is due to the inherent stochasticity of vehicle arrivals and emissions rates. Using current tools, even detailed knowledge of on-road vehicles and traffic leaves uncertainty in short-term roadway emissions.