Graph-based modelling and AI for multi-modal transport resilience under urban overheating
Project ID: 2531bd1613
(You will need this ID for your application)
Research Theme: Digital Security and Resilience
Research Area(s):
Infrastructure and urban systems
Building a secure and resilient worldstrategic theme
AI and Data Science for Engineering, Health and Government (ASG)
UCL Lead department: Bartlett School of Sustainable Construction
Lead Supervisor: Abdul-Majeed Mahamadu
Project Summary:
Why this research is important
As cities grow denser and hotter, overheating is becoming one of the most pressing challenges for urban infrastructure. High temperatures can force underground metro systems to close, creating major disruptions to surface transport such as buses, trams, and cycling routes. Understanding how these networks can work together adaptively during heat-related disruptions is crucial for creating climate-resilient cities. This project will explore how artificial intelligence and graph-based modelling can help predict, analyse, and enhance the coordination between underground and surface transport systems when extreme heat strikes.
Who you will be working with
The project will be based at University College London, within the Bartlett School of Sustainable Construction. You will join an interdisciplinary research environment that connects civil engineering, urban systems modelling, and data science. The supervisory team brings expertise in infrastructure resilience, digital twins, and AI-based transport modelling, providing close academic and technical support. You will also have opportunities to engage with industry partners such as Transport for London (TfL) and other stakeholders in urban mobility.
What you will be doing
You will develop a computational framework to represent urban transport as interconnected graph networks. Using real-world datasets (e.g. temperature, bus routes, ridership, cycleway, and walkway), you will identify vulnerable nodes, model cascading failures, and test adaptive strategies such as rerouting and service reconfiguration. Along the way, you will develop skills in AI and data analytics, graph neural networks, probabilistic modelling, programming, and system simulation. You will also learn to interpret complex datasets and translate analytical insights into practical recommendations for more resilient transport operations.
Who we are looking for
We welcome applicants with backgrounds in civil engineering, computer science, urban systems, or related disciplines. Strong analytical skills, curiosity about AI and sustainable infrastructure, and motivation to work across disciplines are highly valued.