Telematics and Police Driving: Using Data and AI to Reduce Risks Keele University in United Kingdom

This funded PhD studentship at Keele University investigates the risks associated with police driving, leveraging telematics data and artificial intelligence to proactively reduce incidents and improve safety for officers and the public. The project is a collaboration with the Staffordshire Police Road Crime Team and potentially other UK police forces, aiming to profile driver risk, quantify organizational effects such as fatigue and shift patterns, and develop real-time AI tools to predict and mitigate high-risk events and collisions. The research will also deliver an ethical and legal framework for telematics data use in policing, ensuring responsible and transparent deployment.

The project adopts a mixed-methods approach, integrating quantitative telematics data and qualitative contextual inputs from officer interviews, alongside document analysis of policy materials and regulatory guidance. Predictive modelling will utilize interpretable supervised learning methods, including logistic regression, random forests, and gradient boosting, with a focus on transparency and explainability. Deep learning may be applied for temporal pattern recognition in sequential driving data. Qualitative socio-technical analysis will explore organizational and personal factors influencing risky driving, culminating in the development of a situational risk index. An AI-based decision-support tool will be prototyped, combining driver risk scores, driving risk profiles, and situational risk predictions, with built-in safeguards for robustness and responsible deployment. The tool will be designed in collaboration with police stakeholders to ensure operational relevance.

Ethical, legal, and policy analysis will assess privacy, data governance, fairness, accountability, and transparency in telematics-based and AI-supported decision-making. Stakeholder engagement will inform governance guidelines for lawful, ethical, and transparent implementation. The project’s objectives include developing predictive driver and driving risk profiles, modeling organizational and personal factors, prototyping an AI decision-support tool, and producing governance guidelines for telematics use in policing.

Applicants should hold a Bachelor’s degree at first or upper second class level in Data Science, Artificial Intelligence, Computer Science, Cyber Security, Digital Forensics, Information Systems, Engineering (Systems, Transport, or Software), Criminology, Policing Studies, or Social Science with strong quantitative components. A Master’s degree in a related field is highly desirable but not mandatory. The studentship covers home tuition fees in full for 3.5 years or 1+3.5 years at £5,006 per annum for 2025/26, with the exact fee rate for 2026/27 to be confirmed. The University will waive the additional international fee for successful UKRI DTP-funded studentship applicants. Stipend support is provided at £20,780 per annum for 2025/26, with the rate for 2026/27 subject to confirmation. All funded PhD researchers, whether UK or international, are eligible for a full award at the UKRI rate.

To apply, candidates should submit their application via the Keele University studentships page, ensuring two letter-head references with written signatures are uploaded or emailed by referees before the deadline. For enquiries about the vacancy, shortlisting, or interviews, contact Dr Helen Wells at [email protected]. The application deadline is March 18, 2026.