Alina Patelli
    • School of Engineering and Applied Science, Aston University

      B4 7ET Birmingham

      United Kingdom

    Accepting PhD Students

    PhD projects

    Transfer Learning Solutions for Intelligent Transportation (Artificial Intelligence, Intelligent Transportation, Smart Cities) Traditional urban infrastructure is at a breaking point. Given the rampant pollution, traffic congestion, and rising cost of powering energy-inefficient structures that are commonplace in our cities, switching to a more sustainable technological platform to support urban life is an imperative. Smart cities offer a potential solution, with intelligent transportation spearheading the transition from traditional urban traffic monitoring and control to a greener, more cost-effective approach, seamlessly integrated in the urban ecosystem. Within this context, our project aims at measurably improving transportation related decision making at every level, from finding the fastest route to work in real time and improving traffic light control, to investing in road infrastructure. The project’s stakeholders, i.e., ASTUTE, Birmingham City Council, and the Department for Transport, will be regularly consulted to inform project work and evaluate its results. The initial modelling of urban traffic will draw from the supervisor’s recent work, leveraging several sources of open data. The in-depth investigation and comparison of current modelling and prediction techniques, alongside their applications, will lay the groundwork for the original contribution to science, namely new, accurate, robust, scalable, and economical urban traffic prediction algorithms. The PhD researcher will investigate several distinct approaches to modelling and prediction, including time series, deep learning, and evolutionary computation. The algorithms will feature modern transfer learning capabilities, in order to reuse existing knowledge effectively and efficiently. Apply here: https://www.aston.ac.uk/study/courses/phd-engineering-and-applied-science

    20152020

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    Engineering & Materials Science

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