[UK Only] Fully funded PhD: Scalable Automated Planning Approaches for Urban Traffic Control
Mauro Vallati
Fully funded PhD: Scalable Automated Planning Approaches for Urban Traffic Control
Deadline: 1st August 2025
PhD Start date: 1st October 2025
Please note: This call is open to UK Applicants only.
Project Introduction
In the UK, with 32.5 million vehicles on the roads, congestion is a growing problem, costing the economy over £8 billion annually and each citizen an average of 115 hours stuck in traffic every year. Beyond the economic drain, urban traffic significantly impacts our quality of life and poses serious health risks.
Artificial Intelligence approaches, particularly automated planning techniques, have already proven effective in optimising traffic light timings to manage both everyday patterns and unusual traffic conditions. The techniques developed by the AI4UTMC research team of the University of Huddersfield have been deployed to control traffic after recent concerts at the John Smith Stadium, as well as in urban regions of England. They can efficiently achieve specific goals set by transport engineers, like reducing delays or prioritizing emergency vehicles.
However, a major issue is that existing AI planning approaches are currently able to control only limited urban areas, typically just a few interconnected junctions.
This project aims to overcome this limitation by designing and developing innovative solutions to allow AI planning to control traffic across entire cities, unlocking the full potential of smart urban traffic management for widespread benefit.
Project Details
This project will address the critical challenge of extending the capabilities of Artificial Intelligence planning systems to manage traffic across entire urban regions. While AI planning has proven effective in optimising traffic signal controlling within small areas of cities, its application to large-scale city networks is currently limited by the complexity and size of the required planning models.
The core of this project involves advancing the field of Knowledge Engineering for Planning and Scheduling specifically for the urban traffic control (UTC) domain. The aim is to develop methodologies for creating, maintaining, and validating planning domain models that can accurately represent the complex dynamics of city-wide traffic while remaining computationally tractable for automated planners.
Project-Specific Entry Requirements
This project requires an exceptional candidate with a strong knowledge of Computer Science topics, particular AI and symbolic AI, and prior experience or significant interest in Smart Transportation.
Location: Huddersfield.
Funding: 3 years full time research covering tuition fees and a tax-free bursary (stipend) starting at £20,780 for 2025/26 and increasing in line with the EPSRC guidelines for the subsequent years. Funded via the Engineering and Physical Sciences Research Council Doctoral Training Programme.
Duration: 3 years full-time plus 12 months writing up (please note that no funding is available for the writing up period).
Application instructions
1. Download the Expression of Interest Form 2025 at: https://www.hud.ac.uk/postgraduate/research/research-scholarships/epsrc-phd-studentships/
2. Provide copies of transcripts and certificates of all relevant academic and/or any professional qualifications.
3. Provide references from two individuals. Completed forms, including all relevant documents should be submitted via-email to pgrscho...@hud.ac.uk
Please note that this is a competitive application process which will include an
interview. Shortlisted candidates will be contacted for an interview in person or via Teams. After interview the most outstanding applicants will be offered a studentship.
Queries about the application process are welcome and should be emailed to
Informal enquiries about this project should be directed to Prof. Mauro Vallati m.va...@hud.ac.uk