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Thesis title: Estimating Road Vehicle Traffic Flows and Air Pollutants in Great Britain: A Data Mining Perspective
An expanding street network, coupled with an increasing number of vehicles testifies the significance and dependence on road transportation of modern economies. Unfortunately, increasing use of road transport comes with its drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to the country’s objectives for Air Quality standards and a barrier to the ambitious targets to reduce its emissions.
Unsurprisingly, traffic flow forecasting, environmental impacts as well as potential future configuration of road transport are just some of the topics which have received a great of attention in the transport literature. However, assessment of the determinants of traffic flows has been commonly restricted to specific, normally urban areas, while road transport emission studies do not take into account a large part of the road network, usually focused on major arteries.
This research aims to contribute in the field of road transportation studies, by firstly identifying associations among different factors influencing the use of and demand for road transportation systems across Great Britain, secondly by estimating air pollutant emissions, known to be responsible for negative impacts on human health and ecosystems and finally, by identifying the potential air pollutant abatement from technological developments of road vehicles and policy development.
In particular, this study aims to identify and clarify the degree of influence at which specific factors affect traffic flows as well as the road transport sector emissions of specific air pollutants, at each road link in Great Britain – an unprecedented level of detail. The thesis concludes with the analysis of transport scenarios to assess future impacts on air quality. This is achieved by employing spatial data and applying state of the art (geo)statistical and machine learning methodologies.
Biography
Alexandros is a transport analyst with specialization in road transport modelling, urban design and transport implications on air quality and climate change. Prior joining UCL ISR Alexandros has completed an MSc in GIS from UCL, an MSc in Sustainable Energies from Brunel University and a BSc from the Technological Education Institute in Greece. In the past he has contributed in the development of a research paper for the identification of abnormal vessel behaviour in the East Mediterranean in collaboration with the European Commission’s Joint Research Centre (JRC). He has also worked as a researcher on renewable energy, and climate change in the UK as well as a data analyst and renewable energy engineer in Greece. His main academic interests include spatial and spatio-temporal analytics, GIS, machine learning and data mining.
Publications
- 2020, “Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling”, Journal of Transport Geography 83, 102658, with Paolo Agnolucci
- 2017, “Detecting Vessels Carrying Migrants Using Machine Learning”, in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2nd International Symposium on Spatiotemporal Computing 2017, Volume IV-4/W2. (10 2017) 53–60, with Tao Cheng and Michele Vespe