Real-Time Vehicle Tracking for Smarter Transport Decisions

About the Project

Location
This position will be based on the Aston Campus in Birmingham, UK. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Project
Vehicles equipped with tracking technologies generate vast amounts of data, offering insights for transport operations, planning, and safety. Aston’s expertise in machine learning, AI, and transport engineering will unlock this data’s full value, optimising operations, enhancing safety, and driving sustainable mobility solutions. This will involve research challenges such as integrating and managing large-scale datasets, as well as ensuring privacy and security compliance.

This project aims to use vehicle tracking data for optimising transport operations, planning, and safety through analytical methods with the following specific objectives:

1. Develop privacy-compliant methods for integrating and standardising data from multiple sources.
2. Build accurate, scalable multi-agent models for predictive traffic analytics using multi-source data.
3. Design real-time optimisation and autonomous co-ordination algorithms that incorporate external factors like weather and infrastructure conditions.

By unlocking the full potential of these datasets, the research will revolutionise traditional transport modelling, enabling more accurate decision-making and new applications in transport planning.

What Makes This Project Unique

This project combines industry data (Mobito), deployment opportunities (e.g., Coventry City Council trials), and Aston’s ACAIRA centre’s AI resources. Candidates will pioneer solutions with impact, supported by a globally recognised supervisory team.

Supervisory Team & Research Environment
The supervisory team brings complementary expertise in intelligent transport systems, machine learning, and agent-based modelling. Dr Tong has over 25 years of experiences in traffic engineering and transport modelling. He is a known expert in analysing vehicle tracking data to develop driving cycles that have been adopted by governments and academics. Dr Chli leads a research group with an international reputation in AI for smart cities, including deep reinforcement learning for traffic optimisation and agent-based modelling for policy and infrastructure simulation. Her recent work deployed in Coventry City Council—has received national and international attention (BBC News, Deutsche Welle, Radio NZ).

The successful candidate will benefit from the dynamic environment of Aston’s Centre for Artificial Intelligence Research and Application, and the transport research group. Interaction with active Knowledge Transfer Partnerships in related topics, real-world datasets, and cutting-edge AI technologies will support impactful, publishable research with strong translational value.

Industry Partnership with Mobito:
Mobito, a leading European data mobility platform, will provide exclusive access to high-quality, real-world datasets and valuable industry insights. This collaboration enhances the real-world impact of the research and strengthens the link with commercial deployment.

Data Sources and Technologies:
The project will utilise diverse, high-resolution datasets from vehicles, sensors, and open-source transport feeds. Core technologies include Python, PyTorch for machine learning, geospatial data platforms, and cloud-based processing frameworks such as AWS or Azure. Access to Mobito’s mobility data marketplace may provide rare and commercially valuable datasets.

Industry Exposure and Opportunities:
There will be opportunities to engage directly with Mobito’s data science and business teams through collaborative sessions, internship opportunities, and visits to Mobito’s offices.

Candidate Development Opportunities:

• Experience developing and deploying AI algorithms in real-world traffic systems.
• Exposure to cutting-edge AI methods, including transfer learning, agent-based modelling, and simulation synthesis.
• Opportunity to co-author with a team recognised for top-tier publications (e.g., AAMAS, ITSC, JAIR, TRA, TRC).
• Support for attending high-profile conferences and AI policy forums (e.g., Turing Institute’s AI UK, IEEE ITSC, TRB Annual conference, HKSTS).
• Mentorship within a globally connected AI research team with a strong industrial and civic partnership network, boosting employability and innovation skills.

Person Specification
Candidates should have been awarded, or expect to achieve, EITHER:

• A Bachelors degree in a relevant subject with an award of First Class or 2.1.

OR

• A Bachelors degree in a relevant subject with an award of First Class or 2.1, and a Masters degree in a relevant subject with an award of Merit or higher.

Qualifications from other countries which are considered by Aston University to be equivalent to that described above will be eligible to apply.

Desirable:

• Strong programming
• Strong interest in machine learning and transport systems
• Masters in a relevant subject (Merit or higher)
• Experience with deep reinforcement learning
• Familiarity with traffic engineering or smart mobility frameworks

Contact
For formal enquiries about this project contact Dr Hing Yan Tong at h.tong1@aston.ac.uk

Apply
We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:
1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

Interviews
Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Funding Notes
This position covers the tuition fees for the duration of the programme.