Meet our researcher Wenhua Jiang

Wenhua Jiang is a researcher with a background in transport modelling and data science. We asked her about her role at TransiT.

What’s your role at TransiT?

I’m a Postdoctoral Research Associate at TransiT, working with the team on Challenge Led Demonstrator 2, which focuses on decarbonising road and rail passenger transport in the West Midlands.

We use agent-based modelling to study how people move around transport networks and to explore how different interventions could encourage shifts towards low-carbon travel. For example, the model allows us to test scenarios such as new policies, incentives, or infrastructure – and assess how these might influence travel behaviour.

By modelling how individual travellers make decisions, such as whether to take the bus or drive, we can explore how travel demand might shift under different scenarios.

Wenhua Jiang

What is agent-based modelling?

In simple terms, agent-based modelling is a way of building computer simulations made up of many individual ‘agents.’ These agents can represent people, vehicles, or organisations. The model examines how agents make decisions, interact with each other and respond to their environment. Its strength lies in capturing individual differences in behaviour, which makes the outcomes more realistic than many other modelling techniques.

At TransiT, I apply this approach to road–rail passenger mobility in the West Midlands. By modelling how individual travellers make decisions, such as whether to take the bus or drive, we can explore how travel demand might shift under different scenarios. For this project, we use a synthetic population built from real-world data sources, including the UK Census and the National Travel Survey, an annual household survey that collects detailed information on personal travel across England.

Tell us a bit about yourself?

I completed a Master’s in Transportation Planning and Management at Tongji University in Shanghai, followed by two years as an engineer at the China Railway Shanghai Design Institute, working on railway and urban transit systems. I then earned a PhD in Transportation Engineering at Monash University in Melbourne, focusing on passenger demand prediction and mobility management in urban transit.

Since then, I’ve held postdoctoral positions at the University of Edinburgh, applying deep learning to health data, and at the Alan Turing Institute in London, where I worked with the UK Department for Transport on agent-based modelling of the charging network for electric heavy goods vehicles.

What do you hope to achieve at TransiT?

There are two main aspects. First, on the technical side, my background is in transport modelling, engineering, and data science, and I’m excited to learn from our multidisciplinary team, which includes experts in software engineering, human factors, and ontology. I’d like to strengthen my technical skills by collaborating closely with them.

Second, I see TransiT as a step forward in my academic career. I want to develop grant-writing experience, contribute to high-quality research publications, and lay the foundations to become an independent researcher and, eventually, a principal investigator.