Simulating an entire country's daily commute and why a supercomputer helps

��Vlog

Every morning, people across Luxembourg make the same kinds of decisions: Should I take the train? Drive? Catch the bus? These individual choices, multiplied across an entire population, shape whether roads clog, trains run full, and new infrastructure pays off. Understanding how transport systems behave, how they can be simulated, and improved is the research topic of the Transport Research Group (MobiLab) of Prof. Francesco Viti and his former colleague .

High Performance Computing plays a key role in this work. The team uses advanced computational tools. One common approach treats people as independent “agents” in a simulated world, making decisions, responding to their environment, and interacting with everyone else simultaneously. The result is a mobility Digital Twin of the Grand-Duchy of Luxembourg.

A country in 100 zones

To create a simulation of Luxembourg’s mobility network, the country gets divided into up to 100 zones. For each pair of zones, they determine how many people travel between them and how long those journeys take. This creates an origin-destination matrix, which is essentially a large table of all itineraries.

Based on these and other publicly available data, they then create with their model a synthetic population which is representative of the real population. It consists of moving virtual residents, each with their own travel patterns, preferences, and constraints.

Asking “what if?”

Simulations are often used to play through scenarios that are hard or impossible to create in real life. The same applies to the mobility sector. By tweaking the conditions inside the simulation, researchers can test various scenarios: What changed after public transport became free for the public in Luxembourg? What is the impact of a major concert at Rockhal on traffic? Where should a new tram line be added?

These are exactly the questions planners and policymakers need answered before committing to expensive infrastructure or pricing reforms.

When the bus doesn’t wait for the train

The MobiLab team also addresses problems that are familiar to anyone who has missed a connecting service: poor synchronisation between transport modes. The research focused on a rural on-demand shuttle, which is a small bus that picks up and drops off passengers when requested. The idea was to investigate whether this approach is more efficient than the existing bus with a fixed schedule, slightly misaligned with train arrivals.

Across multiple simulation runs with different assumed passenger behaviours, the findings showed that the fixed-schedule service was failing to capture around half of its potential ridership. By switching to a demand-responsive model, over 90 percent of potential passengers could be served. The simulation helped to study a problem that would have taken years of real-world data to diagnose.

From academia to industry

The same simulation framework that underpins this academic work is now finding commercial applications. A spin-off project from the ��Vlog is using this technology to build “digital twins” that stakeholders can query in real time to support operational decisions. Instead of relying on expensive external data providers or static planning reports, clients can run their own scenarios and get answers tailored to their specific context.

This shift from lab to market has been enabled partly by access to the high-performance computing infrastructure at the University, providing the necessary data for the enterprise.

Why a supercomputer?

Keeping track of a country’s transport network, with all its individuals navigating the digital world, interacting with traffic and one another, is extraordinarily demanding. On a standard desktop computer, a single simulation run takes somewhere between 15 and 20 days, hoping that the computer doesn’t shut down. Federico Bigi, a former member of the MobiLab group, even mentioned that the computational load was so intense that one normal computer partially broke at some point.

The simulation software demands both significant memory and many parallel processors simultaneously. High-performance computing (HPC) clusters address both constraints (see also the previous article on HPC). On the HPC infrastructure available in Luxembourg, simulation time drops to roughly four days or less. A desperately needed reduction and guaranteed system stability, especially given that fresh simulations are needed on a roughly monthly basis to reflect updated conditions.

What is clear is that this kind of research was simply not possible a decade ago. The combination of sufficiently large HPC clusters, mature modelling software, and rich mobility data has only recently converged to make nationwide transport simulation tractable. The digital twin of Luxembourg, running on a supercomputer, asking “what if?”-scenarios on behalf of planners and commuters alike, is a perfect example of how simulations powered by HPC help improve our everyday life in perhaps unexpected ways.