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 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/

Entry Requirements

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

Closing date: 8 April 2024. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.

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 “iPhD Sustainable AI (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Selin Ahipasaoglu.

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