Real-Time Vehicle Tracking for Smarter Transport Decisions Aston University in United Kingdom

This PhD position at Aston University focuses on real-time vehicle tracking to enable smarter transport decisions, leveraging advanced machine learning and artificial intelligence techniques. Based at the Aston Campus in Birmingham, UK, the successful candidate will work closely with the Centre for Artificial Intelligence Research and Application (ACAIRA) and the transport research group, benefiting from a dynamic, collaborative research environment.

The project aims to optimise transport operations, planning, and safety by analysing large-scale vehicle tracking datasets. Key objectives include developing privacy-compliant methods for integrating and standardising data from multiple sources, building scalable multi-agent models for predictive traffic analytics, and designing real-time optimisation and autonomous coordination algorithms that account for external factors such as weather and infrastructure conditions. The research will revolutionise traditional transport modelling, enabling more accurate decision-making and innovative applications in transport planning.

Unique aspects of this project include access to industry data from Mobito, deployment opportunities with Coventry City Council, and support from Aston’s ACAIRA centre’s AI resources. The candidate will pioneer impactful solutions, supported by a globally recognised supervisory team. Dr Hing Yan Tong, with over 25 years of experience in traffic engineering and transport modelling, leads the project. The research environment offers opportunities for collaboration with Knowledge Transfer Partnerships, access to real-world datasets, and cutting-edge AI technologies.

Industry partnership with Mobito provides exclusive access to high-quality, real-world datasets and valuable industry insights, enhancing the practical impact of the research and strengthening links with commercial deployment. The project utilises diverse, high-resolution datasets from vehicles, sensors, and open-source transport feeds, employing technologies such as Python, PyTorch, geospatial data platforms, and cloud-based processing frameworks (AWS, Azure).

Candidate development opportunities include experience in developing and deploying AI algorithms in real-world traffic systems, exposure to advanced AI methods (transfer learning, agent-based modelling, simulation synthesis), co-authoring with a team recognised for top-tier publications, and support for attending high-profile conferences and AI policy forums. Mentorship within a globally connected AI research team with strong industrial and civic partnerships will boost employability and innovation skills.

Applicants must have a First Class or 2.1 Bachelor’s degree in a relevant subject, or a First Class or 2.1 Bachelor’s degree plus a Master’s degree (Merit or higher) in a relevant subject. Equivalent international qualifications are accepted. Desirable attributes include strong programming skills, interest in machine learning and transport systems, a Master’s degree (Merit or higher), experience with deep reinforcement learning, and familiarity with traffic engineering or smart mobility frameworks. English language proficiency is required; evidence can be submitted later if not currently met.

Funding covers tuition fees for the duration of the programme. Applications must be complete and include transcripts, certificates, research statement, personal statement, CV, two academic references, evidence of English language proficiency, and a copy of your passport. Interviews will be conducted online via Microsoft Teams. For formal enquiries, contact Dr Hing Yan Tong at [email protected]. For application process queries, contact the Postgraduate Admissions team at [email protected].

Apply via the provided link. The deadline for applications is June 1, 2026.