Understanding space-time patterns of vehicular emission flows in urban areas using geospatial technique

Traffic-related emissions are well-known factors in urban environment which may have adverse implication on human health. Estimating vehicular emissions in urban areas provides an understanding of the air pollution caused by traffic. However, existing microscopic approaches cannot simulate the traffic flows and emissions for an entire city and most of the macroscopic approaches are usually highly complex and require priori knowledge about vehicles' route options. This study, therefore, proposes a straightforward and robust approach to simulate vehicular flows and estimated transport emissions at a city scale via a deterministic approach and by applying the Cell Transmission Model (CTM) to simplify the modeling of vehicles' route selections. Under a space-time integrated framework, we firstly simulate a time-dependent distribution of urban vehicular flows and then estimate pollutant emissions of Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Violate Organic Compounds (VOC) for traffic flows on weekday and weekend. Finally, the spatiotemporal patterns of traffic flows as well as traffic emissions were visualized and illustrated under a space-time integrated framework. With accuracies of around 67.4% to 70%, the results demonstrated the feasibility of the proposed approach for estimating city-scale traffic flows and emissions from road transport.