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

8 Conference publication
1 Chapter (peer-reviewed)
1 Article

8 results

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2023
Predicting Normal and Anomalous Urban Traffic with Vectorial Genetic Programming and Transfer Learning
Hamilton, J. R., Ekárt, A. & Patelli, A., 9 Apr 2023, International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Correia, J., Smith, S. & Qaddoura, R. (eds.). 1 ed. p. 519–535 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13989 LNCS).
Research output: Chapter in Book/Published conference output › Conference publication

Open Access
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2022
A GENTLER Approach to Urban Traffic Modelling and Prediction
Patelli, A., Hamilton, J. R., Lush, V. & Ekart, A., 23 Jul 2022, 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. IEEE, p. 1-8 8 p. 9870273
Research output: Chapter in Book/Published conference output › Conference publication
2020
Genetic Programming with Transfer Learning for Urban Traffic Modelling and Prediction
Ekárt, A., Patelli, A., Lush, V. & Ilie-Zudor, E., 3 Sept 2020, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. IEEE, 9185880
Research output: Chapter in Book/Published conference output › Conference publication
How to Successfully Run a Digital Apprenticeship: The Programming Boot Camp Case Study
Patelli, A., Beaumont, T. & James, G., 10 Jan 2020, Proceedings - 4th Conference on Computing Education Practice, CEP 2020. ACM, p. 1-4 3. (ACM International Conference Proceeding Series).
Research output: Chapter in Book/Published conference output › Conference publication
2019
Disobedience as a mechanism of change
Burth Kurka, D., Pitt, J., Lewis, P. R., Patelli, A. & Ekart, A., 15 Jan 2019, Proceedings - 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018. IEEE, Vol. 2018-September. p. 1-10 10 p. 8613714. ( 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)).
Research output: Chapter in Book/Published conference output › Conference publication
2017
Gaining insights into road traffic data through genetic improvement
Ekárt, A., Patelli, A., Lush, V. & Ilie-Zudor, E., 15 Jul 2017, GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference. New York, NY (US): ACM, p. 1511-1512 2 p.
Research output: Chapter in Book/Published conference output › Conference publication

Open Access
File
2016
Autonomic curation of crowdsourced knowledge: the case of career data management
Patelli, A., Lewis, P. R., Wang, H., Nabney, I., Bennett, D., Lucas, R. & Coles, A., 8 Dec 2016, Proceedings : 2016 International Conference on Cloud and Autonomic Computing: co-located with the Tenth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2016), ICCAC 2016. IEEE, p. 40-49 10 p.
Research output: Chapter in Book/Published conference output › Conference publication
2015

Two-B or not two-B? Design patterns for hybrid metaheuristics
Patelli, A., Bencomo, N., Ekart, A., Goldingay, H. & Lewis, P., 11 Jul 2015, GECCO Companion '15 : proceedings of the companion publication of the 2015 annual conference on Genetic and Evolutionary Computation. Silva, S. (ed.). New York, NY (US): ACM, p. 1269-1274 6 p.
Research output: Chapter in Book/Published conference output › Conference publication

Alina Patelli

Research output

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