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Supervisor: Professor Ying Jin


Research overview:

Shanshan Xie works on developing mathematical models and estimation algorithms that assist in data-led estimation, decision-making and policymaking for transport and land-use planning.

In her PhD programme, she develops discrete choice models that help predict the travel behaviour of early adopters of new ways of mobility under the flexible working trend, with limited data availability. The models incorporate flexible model parameters. And a new Bayesian assimilation approach is developed to update choice models timely as new data become available.

She is actively involved in research projects of the Cities and Transport Group at the Martin Centre. The scales of her previous projects are from urban to national levels. The data include national, city level and MSOA level travel survey data; traffic data such as License Plate data and GPS data; economic data such as Business Register and Employment survey data, etc.

In terms of research interests, her focus has been on building flexibility and responsiveness of the models to accommodate future uncertainty in transport and lan-use planning. Regarding research skills, she is interested in data processing, Bayesian Inference, Discrete Choice Models and Machine Learning techniques.



Shanshan Xie’s research concerns modelling, simulation, and interpretation of human behaviour in the urban system, with a particular interest in transport modelling. Her PhD thesis investigates heterogeneous travel behaviour through Bayesian modelling. She has been actively participated in modelling applications. Her recent work includes urban analytics for the Cambridge team in the Royal Society’s Rapid Assistance in the Modelling the Pandemic programme, and an estimation of car reduction if substituting shared connected cars for private cars in Shenzhen, China.