Modelling and visualisation to support decision-making in air quality-related transport planning

This thesis introduces three main elements to support decision-making in air quality-related transport planning. The first are novel automatic collection and processing algorithms for traffic flow and geospatial data for input to air pollution models of transport schemes under analysis. The second is a novel strategy to improve the modelling of air quality by the calibration of input background concentrations. The third is a novel 3D air pollution dispersion interface for the 3D visualisation of the air quality predictions in 3D digital city models. Four urban transport schemes were used for the initial development of, and for testing, the applicability and validation of future air quality predictions of the decision-support system based on the above three elements. The automation of the input data collection and processing reduced significantly the time and effort required to set up the air pollution model. The calibration of background concentrations significantly improved the accuracy of, not only the annual mean, but also the hourly, air quality predictions and effectively reduced the model runtime. The 3D air pollution dispersion interface provided an intuitive 3D visualisation of the air quality predictions at and above the ground surface in a single 3D virtual scene. The application of this decision-support system enabled the development of alternative future traffic scenarios so a proposed urban transport scheme might contribute to achieving certain air quality objectives. The validation of the future air quality predictions showed that the methods used for the future projection of air pollution input data slightly increase the error between the modelled and actual annual mean NO2 future concentrations. They also significantly increase the error between the modelled and actual hourly NO2 future concentration