###Generative scenarios for getting Transport to an equitable Net Zero – flipping modelling on its head using Machine Learning
Project ID: 2228bd1202 (You will need this ID for your application)
Research Theme: Energy and Decarbonisation
UCL Lead department: Centre for Advanced Spatial Analysis
Lead Supervisor: Adam Dennett
Project Summary:
It is clear we do not have a tangible plan to get to Net-Zero in the UK Transport sector. We need better tools to help identify how we may reach our carbon reduction goals in the transportation sector.
We propose to flip modelling on its head, rather than using models to test scenarios conceived by others, we may use modelling to suggest and generate interventions based on clear outcomes (for example equitable carbon reductions). The methodology would involve assessing what aspects of recent advances in machine learning may be applicable to this problem set. For example, variational autoencoders bring together deep learning and graphical models which offer exciting possibilities when applied to network typologies such as those of a multi-modal transportation network. These algorithms may be applied to novel Agent Based representations of the country to contribute to an active policy debate.
For example, consider Brighton, where we have a mix of affluent London commuters and pockets of deprivation, with a car centric city centre. How might we level-up access to economic opportunity whilst reducing emissions and improving air quality? These algorithms would be used to suggest interventions such as new public transport services (bus routes), new fare structures (capped or discounted fares) or the reallocation of road space for active modes (cycle lanes and low traffic neighbourhoods).
We are looking for either someone from an urban, planning or transport background with some experience of data science, or mathematics, physics or computer scientist background with some interest in the domain of city modelling. This is an interdisciplinary project looking at bringing domains together to tackle an existential challenge.