Using AI to make road fleet operations more efficient, safe, and sustainable

Road-based vehicle fleets are the cornerstone of modern-day transport and logistics systems, supporting a wide range of passenger and freight travel needs. From public buses and Heavy Goods Vehicles (HGVs) operating in interurban environments to taxis and cargo cycles serving dense city cores, fleets represent a sizeable proportion of traffic on the roads, and can therefore be attributed a considerable share of the resulting adverse impacts, such as congestion, accidents, energy consumption, pollution, and noise. Addressing these impacts at the source, i.e., at the individual vehicle and driver/operator level, can, therefore, deliver substantial benefits for the whole of the transport system. Such an endeavour, however, has not been fruitful to date due to a prevailing lack of methods and tools aimed at understanding the effects of different vehicle- and driver-related parameters on the efficiency, safety, and sustainability of fleet operations. The aim of this project is, hence, to leverage the potential of big data and AI to obtain a clearer insight into such effects, including, for example, vehicle technical characteristics and driver/operator moods, preferences, and behaviours, and use these insights to improve existing operational and strategic policies. To this end, we plan to utilise relevant data from existing large vehicle fleets to develop models that will be integrated into a prototype training platform to be used across different fleet operators in the UK and internationally. The models will integrate optimisation under uncertainty ( e.g., Markov decision processes ), preference/choice modelling analytics and operational research to embed meaningful decision support into the training platform and derive useful insights from the dataset.

We are looking for an exceptional PhD candidate with a background in applied mathematics, computer science, or engineering with advanced programming skills, ability to work within a large multidisciplinary team, excellent written and oral presentation skills, and desire and ability to distribute the outputs of the research into various platforms. 

This is a 4-year integrated PhD (iPhD) programme and is part of the UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) under the theme AI for Transportation and Logistics https://sustai.info/.

Entry Requirements

You must already have, or expect to shortly graduate with, a very good undergraduate degree or Master’s degree (at least a UK 2:1 honours degree) or international equivalent in BSc or MSc in Computer Science, AI, Operations Research, Economics, Applied Mathematics, or a related discipline. All applications will be considered on merit, tailored to the individual case. 

Please note that only UK, EU and Horizon Europe students are eligible for this PhD studentship.

How To Apply

Apply online: Search for a Postgraduate Programme of Study

Select programme type Research, Full or Part time, choose the 2025-2026 Academic year, and in the Search text enter “sust”.

On the next page, select Apply Online for the “iPhD AI for Sustainability”.

In Section 2 of the application form you should include the name of the project in the Area of Research.

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

IELTS 6.5 (or equivalent) for non-native English speakers