Mathematics and the future of rail modelling

Bachelor of Mathematics (Honours) student Dylan Wildman is taking his passion for mathematical modelling and transport systems to new territory, developing a sophisticated simulation of Brisbane’s inner‑city rail network. His project, supervised within QUT’s Operations Research group, blends mathematics, programming and real‑world data to better understand how high‑demand rail systems behave under pressure.

A mathematical pathway into real‑world impact

Dylan was drawn to using the DesRail simulation library for his project. DesRail is an open‑source simulation library developed at QUT, led by Professor Paul Corry, that allows researchers to recreate real rail networks, test service patterns and analyse how trains move through complex infrastructure.

“It allows me to apply my mathematical and computer programming skills in a practical way,” he said. “Because the simulation is flexible and scalable, it’s valuable in contexts like scheduling optimisation and infrastructure investment decisions.”

Working with supervisor, Professor Professor Paul Corry, who brings deep industry experience in Operations Research was another major drawcard.

_{Video showing Dylan's use of the DesRail simulation library to configure a simulation model of the
inner Brisbane urban network for his Honours project on simulation analysis and simulation-optimisation.}

Building a digital model of Brisbane’s busiest rail corridor

Dylan’s Honours project focuses on simulating the movement of passenger trains through the highest‑demand section of the Brisbane network.

The model is designed to:

• Replicate real train movements through the inner‑city corridor
• Analyse throughput capacity, average speeds and congestion patterns
• Test how the network behaves under disruption, especially when already operating at maximum load

This kind of modelling helps researchers and planners understand how tightly coupled systems respond to stress, which is a crucial step in improving reliability and resilience.

Automating a complex network

One of the most time‑consuming challenges in rail simulation is preparing the underlying network data. Dylan’s project incorporates an automated workflow that converts the publicly available Queensland Rail Network GIS data into a simulation‑ready format.

“Manually configuring the network can be extremely time‑consuming,” Dylan explained. “By automating the process, we save time and can analyse many different scenarios without having to rebuild the network each time.”

This automation also ensures the simulation respects physical constraints, such as track curvature and segment joins, that determine where trains can realistically travel.

To build the model, Dylan and his supervisor used an AI‑supported workflow that dramatically accelerated the most technical parts of the project. This automation replaced what would normally be days of manual data cleaning and reconstruction.

Once the base network was generated, Dylan layered in the operational logic that the public GIS data doesn’t provide. With AI assistance, he incorporated signal placements at platforms and approaches, and integrated real‑world Queensland Rail timetables to define service patterns. This hybrid approach allowed him to rapidly test multiple scenarios and ensure the simulation behaved in a way that reflected both the physical network and real operating practices.

Looking ahead: a career built on analytical decision‑making

Dylan sees this project as a launchpad for a career focused on data‑driven decision tools.

“My career objective is to create analytical tools that drive better decision‑making. This project has been a great opportunity to showcase and develop my skills in a real‑world setting.”

With Brisbane’s rail network set for major growth in coming years, Dylan’s work highlights the vital role mathematical modelling plays in shaping the transport systems of the future.

This project is just the first stop, Dylan! We’re looking forward to watching your career pick up steam.