This project uses MIDAS road flow counts and speed data to create data-driven traffic assignment models to be applied to national Strategic Road Network for the analysis of flow patterns.
The core aims of this PhD project are to understand spatiotemporal variability of air quality and determine the main drivers of air pollution in urban areas by: (a) deploying a dense network of air quality sensors based on multiple criteria, and (b) employing various air quality modelling and mapping techniques including geostatistical interpolations, statistical and dispersion modelling and data fusion approaches.
Cities have evolved as centres of economic growth but are also responsible for high resource consumption and greenhouse gases emission. In the urban metabolism framework, cities are often described as open systems where the intake of resources is heavily dependent on flows imported from the external environment to sustain economic growth.
Understanding the nuances at play across different spatial scales is of crucial importance when considering urban economic-energetic size-cost performance, specifically when longer-term consequences are considered. Through the application of an allometric understanding of cities, this project offers a more nuanced narrative highlighting the interplay of urban productivity and spatial configurations of human interactions across scales.