Research Fellow in Agent-based Modelling for Future Transportation

University of Leeds

Leeds, United Kingdom

ID: 7086880 (Ref.No. ENVGE1160)
Posted: October 11, 2021

Job Description

Are you an ambitious researcher looking to develop agent-based models of future urban transportation systems? Are you interested in behavioural and social responses to new transportation innovations? Do you want to further your career in one of the UKs leading research-intensive Universities?

We are looking for a Research Fellow with expertise in agent-based modelling and/or transportation modelling to join our international project. The RAIM (Responsible Automation for Inclusive Mobility) project is undertaking qualitative and quantitative analysis of the diverse mobility needs of ageing populations in the UK and Canada, and apply a suite of computational methods to analyse, predict, and optimise an inclusive electric AV (EAV) Demand Responsive Transit (DRT) system design. 

Through a multifaceted methodology, the project is seeking to make advances in our understanding and modelling the complex interactions of socio-economic status, gender, physical and health conditions, lifestyle, and attitudes on spatiotemporal demand for autonomous mobility in older populations. RAIM will furthermore make advances in optimising supply of autonomous vehicles to meet complex, heterogeneous demand, through novel application of Deep Reinforcement Learning and Graph Convolutional Networks. The project will engage closely with transport providers and charitable organisations, and determine the economic and policy case for (or against) public intervention, building estimates of the costs, impact, and broader benefits of EAV DRT schemes under current and future scenarios. 

The RAIM project is a collaboration between the University of Leeds, University of Manitoba, and University College London, and led by Prof Ed Manley in the UK and Dr Babak Mehran in Canada. The project is funded by UKRI and Canadian funding councils under the ‘Canada-UK Artificial Intelligence (AI) Initiative: building competitive and resilient economies and societies through responsible AI’ project call. The project began in 2020 and will run until 2023. Our target sites for analysis are the West Midlands and Winnipeg, Manitoba, Canada. You will have opportunities to spend time working with colleagues on site in Canada.

In this role, you will work on the development of agent-based models of travel demand for proposed EAV DRT systems our UK and Canadian settings. Our estimates of demand will build from qualitative and quantitative analyses, and integrate with models of transport supply, developed by other members of the project team. The agent-based model will incorporate a synthesised population, reflecting heterogeneity in perceptions and willingness to use EAV DRT, and an activity choice model, built from GPS collected survey data. In integrating across different components, the software platform that will be developed will sit at the heart of the project. As such, it is essential that you are able to work closely with colleagues across the project.

To explore the post further or for any queries you may have, please contact:

Professor Ed Manley

Tel: +44 (0)113 343 3356, email: E.J.Manley@Leeds.ac.uk 


Location:  Leeds - Main Campus
Faculty/Service:  Faculty of Environment
School/Institute:  School of Geography
Category:  Research
Grade:  Grade 7
Salary:  £34,304 to £40,927 p.a.
Working Time:  100% - We will consider flexible working arrangements
Post Type:  Full Time
Contract Type:  Fixed Term (for 12 months)
Release Date:  Friday 08 October 2021
Closing Date:  Friday 05 November 2021
Reference:  ENVGE1160


The University community is made up of a wide range of people with diverse backgrounds and circumstances, which we value and regard as a great asset. As part of our continued commitment to equality and inclusion, we strive to create an environment where everyone can reach their full potential and have a real opportunity to participate in and contribute to our activities.



 

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