Using AI To Make Road Fleet Operations More Efficient, Safe, and Sustainable at University of Southampton

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

Supervisory Team: Selin Ahipasaoglu, Ioannis Kaparias, Edilson Arruda, Solar Americas Capital

Project description:

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 project is part of the UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) at the University of Southampton. For more information about SustAI, please see: https://sustai.info/

If you wish to discuss any details of the project informally, please contact Professor Enrico Gerding, Director of the SustAI CDT, Email: sustai@soton.ac.uk.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: 8 April 2024. Later admissions may be considered depending on funds remaining.

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships.  For more information please visit PhD Scholarships | Doctoral College | University of Southampton  Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online:  HERE Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD iPhD AI for Sustainability (Full time)”. In Section 2 of the application form you should include the name of the project as part of the Area of Research and, optionally, specify the supervisor.

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk