Improving public transport through improved data quality

Across the public transport ecosystem, timely and accurate data are essential for improving service quality and meeting the needs of travellers. While real-time data show what is happening on the network minute by minute, scheduled data — the official plans for how services should run each day — form the backbone of operational management.

Luxembourg’s transport operators have been publishing schedule data to data.public.lu for many years. Over time, the scope and richness of these datasets have grown, and with that growth has come the need for more systematic ways to interpret them.

To address this challenge, Luxembourg National Data Service (LNDS) and the Observatoire Digital de la Mobilité (OdM) worked together to automate the transformation of scheduled public transport data into a format that is accessible, consistent, and ready for analysis.

See glossary of key terms and acronyms

National context: a hands-on role in mobility

Digitalisation plays a central role in how public transport services are planned and managed. The most visible aspect of this is probably the real‑time departure data, which track events on the network down to the second. These data are crucial to maintain high levels of service, but it is the scheduled data — the detailed plan of where every vehicle should be and when — that truly underpin the entire operation.

Timetables are to public transport operators what musical scores are to an orchestra: they provide the structure that enables thousands of buses, trains, and trams to operate in harmony across Luxembourg’s networks, all while accommodating operational concerns such as vehicle rotations, driver shifts, refuelling needs, layover times, maintenance windows, and —  most importantly — the service promise made to passengers about frequency, capacity, and connections.

In many larger countries, national authorities primarily define policies and strategies that guide and regulate operations governed at the regional and local level. In Luxembourg, there is no such separation: the Ministry of Mobility and Public Works (MMTP) is the main provider of public transport services in the country. That means MMTP is directly involved in planning the service offer, which includes defining the schedules.

From schedules to service reliability

Creating a schedule is a complex task. Even small errors can ripple through the system. A single missed connection or delayed departure may not bring down the entire network, but it does break the service promise to individual passengers. That directly undermines the policy ambition of MMTP of offering reliable, attractive mobility alternatives.

To safeguard this promise, the government created the “Observatoire Digital de la Mobilité” (OdM) and gave it the mission to ensure that the digital data needed to plan, operate, and improve public transport actually exist, are usable, and meet the highest quality standards.

Collaboration: turning complexity into clarity

As Luxembourg’s scheduled datasets have grown in detail and volume, so has the need for tools that allow analysts to explore them quickly, understand their structure, and relate them to what happens on the network day by day. The ecosystem has reached a point where deeper analysis is both possible and necessary — and where new capabilities are required to make full use of the data already being produced.

This is why OdM joined forces with LNDS: to build the practical capabilities needed to make better use of the scheduled data Luxembourg already publishes. The collaboration set out to create a reliable way to take any published timetable file, inspect it instantly, and compare it with historical operational data whenever needed.

The goal of the project was to turn complexity into clarity. By transforming detailed schedule datasets into a form that is easier to validate, analyse, and interpret, the work strengthens Luxembourg’s ability to understand how its public transport network performs in practice. It equips OdM with the means to monitor data quality more consistently, support evidence‑based planning, and ensure that the service promise made to passengers is grounded in reliable, high‑quality digital information.

LNDS services in action

The core mission of this project was to convert schedule data encoded in NeTEx — an XML-based European data exchange format mandated by the Intelligent Transport System (ITS) Directive — to a more easily consumable tabular data format for OdM.

To that end, we applied several of our services. The Data Quality and Curation Service helped clean and standardise the data, correcting issues such as overlapping schedules or missing values. This work enabled the development of the NeTEx Schedule Converter, which transforms complex NeTEx files into easy-to-read CSV files that are usable by both people and computers. Each file lists stops in the order they are served, showing the full route and the times each stop is reached. This makes it easy to see how services operate, when they operate, and how routes are arranged across the network.

The converter can also create a historical panel that combines all schedule periods for a given bus line into a single file.

At later stages, we added an analytical layer through our Data Visualisation Service, resulting in the Schedule Analyser. This tool highlights differences between timetable versions, organised into clear PDF reports, grouped by bus line, route direction, and day type. Having such a tool is extremely useful because it helps transportation planners quickly visualise and understand changes in service patterns over time, such as which stops were added or removed, how the time between stops or entire trips has shifted, whether some journeys were to be realised in practice, and if the bus is running on schedule.

Overall, the project showed that complex data can be transformed into reliable, accessible formats that directly support better planning, monitoring, and service delivery.

Practical challenges addressed

As part of improving the timetable comparison system, we tackled several issues, including inconsistent bus stop names. Sometimes the same stop appeared with minor differences, such as spelling changes or language variations, which made matching data harder. We solved this by creating a reference list that automatically recognises and standardises stop names, ensuring the stops are always matched correctly.

Turning complex NeTEx files into structured timetables made the data usable not only for systems but also for mobility planners.

— Alexander Mraz, Principal Data Scientist, LNDS

Impact

The impact on public transport management in Luxembourg is significant. The collaboration supported a better understanding of scheduled timings, reduced manual workload, improved data quality, and created a strong foundation for future planning and innovation.

Before automation, processing a single NeTEx file manually took 2–3 hours. With around 800 files, this represented 2–3 months of work. Through LNDS services, robust automated data pipelines were built, reducing processing time to less than 25 minutes in total — close to two NeTEx files per second.

Beyond speed, the project delivered a single, complete, easy-to-use historical table of planned transport data that mobility planners can actually work with. By analysing historical NeTEx data, planners can better understand how schedules evolved over time, identify patterns and trends such as seasonality, complexity, or resource allocation, and validate whether planned network changes were maintained or reversed. This enables long-term analysis and supports more advanced, data-driven planning, including predictive modelling.

This is not only about processing files faster. It’s about asking whether we are planning better networks than before. Historical NeTEx data reveals planning competence, strategic priorities, and institutional learning — beyond day-to-day operations.

— Karthik Arumugam, Data Officer, Luxembourg Ministry of Mobility

Taken together, these outcomes translate into concrete benefits for transport planning and operations:

• Increased efficiency, by automating data extraction and transformation
• Enhanced understanding of data, through standardisation and validation
• More advanced analysis, enabled by clean, structured datasets
• Better operational planning, with improved route and timetable optimisation

Ultimately, travellers benefit from more accurate and up-to-date information, enabling smoother, more reliable journeys — especially across complex or cross-border networks.

Looking ahead

By working directly with the schedule files published over the years, OdM and LNDS were able to uncover what the data already contain, how they behave in practice, and what becomes possible once they are transformed into a clear, consistent structure. It demonstrated that many of the insights needed for planning and monitoring are already present — they simply need to be made accessible.

At the same time, the work highlighted an important lesson for the future: while downstream processing can unlock significant value, the greatest long‑term gains come from strengthening the way data are produced and governed at the source. When datasets are coherent, well‑structured, and aligned with shared standards from the outset, everyone benefits — planners, operators, analysts, and ultimately passengers.

Building on the experience of this collaboration, LNDS is well positioned to support this evolution. For OdM, this means being able to focus more on analysing performance and less on preparing the data needed to do so. For Luxembourg’s mobility system, it means a more robust digital backbone — one that supports better planning, clearer monitoring, and a more reliable service for passengers.

If you would like to learn more about how we could support your project, please reach out to our team through LNDS Service Desk.

Glossary of key terms and acronyms