Dr Yuanxuan Yang

Profile

I am a Lecturer in Data Science of Transport at the Institute for Transport Studies, University of Leeds. My research sits at the intersection of GeoAI and transport equity, two fields that rarely speak to each other as directly as they should. I use spatial data science and computational methods to understand mobility inequality in cities: who gets access to good transport, which neighbourhoods are systematically underserved, and what new forms of data can reveal about the people and places that conventional transport analysis leaves behind.
My work spans travel behaviour analysis, shared and emerging mobility systems, and the development of geospatial AI methods for transport applications. I apply methods ranging from graph-based network analysis and geospatial machine learning to large language models, working with diverse data sources including GPS tracking data, shared mobility records, and user-generated geographic content.
A particular focus of my research is transport equity: understanding how infrastructure, services, and planning decisions shape mobility outcomes differently across social groups and places. I am interested both in developing new analytical tools and in producing findings that are directly useful for transport policy and urban planning.
I welcome enquiries from prospective PhD students and collaborators with interests in transport equity, GeoAI, or data-driven approaches to urban mobility.

Research interests

Transport equity and mobility inequality
Understanding how urban transport systems produce and reproduce spatial inequality. This includes who is systematically excluded from mobility, which places are underserved, and what high-resolution data can reveal about the gap between transport provision and genuine need across diverse social groups and communities.

GeoAI and spatial data science
Developing and applying computational methods including graph-based network analysis, geospatial machine learning, large language models, and spatiotemporal trajectory analysis to extract insight from complex, multi-modal urban and transport datasets such as GPS traces, user-generated geographic content, and unstructured spatial text.

Emerging and sustainable transport systems
Examining adoption, behaviour, and well-being outcomes across new and evolving forms of urban mobility, with a focus on who benefits from emerging transport services and how inclusive design can support broader sustainability transitions in cities.

Co-investigator (Co-I)

• McMatcher: Route detection and Proximity Analysis

Qualifications

• Ph.D. in Geography, University of Leeds

Current postgraduate researchers

• Sijin Wu