When discussing smart mobility, we often think about sensors, digital platforms, or advanced systems. However, an essential part of the transformation happens much earlier: in the ability to understand how a territory actually moves and to use that information to make better decisions. This is where the work of the CRTM, Regional Transport Consortium of Madrid (Consorcio Regional de Transportes de Madrid, CRTM), within DS4MM comes into play.
CRTM is one of the key public actors responsible for planning and coordinating transport across the Madrid region. Its participation in the project brings precisely the territorial and operational perspective needed to connect the value of data with real mobility needs: demand, accessibility, modal integration, and metropolitan planning. In a project such as DS4MM, this connection between strategy and practical application is essential.
A European Project to Bring Data Spaces into Practice
DS4MM was created with a very clear underlying idea: mobility data already exists, but it remains too fragmented. Public administrations, operators, infrastructure managers, and companies generate highly valuable information, yet that information often cannot be easily connected or translated into coordinated decisions. The project focuses precisely on this challenge: how to turn data spaces into useful tools for real-world mobility.
This means going beyond theory. It is not only about discussing interoperability or data sharing, but about testing how this model can work in concrete situations. That is why DS4MM is structured around real pilots and use cases across different territories and mobility domains. These cases make it possible to validate whether the model can genuinely improve planning, anticipation, and coordination.
The Metropolitan Pilot: Madrid and Catalonia as a Planning Laboratory
In line with this approach, the use case involving CRTM is part of the metropolitan pilot project, developed in collaboration with ATM Barcelona and Servei Català de Trànsit. Its focus is on analyzing demand at intermodal transportation hubs in Madrid and Catalonia.
Its focus is the analysis of demand at intermodal transport hubs in Madrid and Catalonia. The objective is to identify optimal locations for new interchange hubs and transfer facilities through a more comprehensive understanding of the territory. To achieve this, the use case integrates variables that are not always analyzed together: mobility networks and flows, accessibility, socioeconomic indicators, land use patterns, and the feasibility of modal integration.
At first glance, this may seem like a purely technical exercise. In reality, however, it addresses a very specific and highly relevant question for any transport system: where does it make the most sense to strengthen connections between modes of transport to improve people’s everyday mobility?
Why This Use Case Explains How DS4MM Works
RTCM’s work with this pilot is an excellent example of how DS4MM is structured as a project.
First, because it starts with a real planning need. Demand is not analyzed for theoretical purposes, but to support decisions that affect the structure of metropolitan mobility and the connections between different transport modes.
Second, because it demonstrates that value does not come from accumulating more data, but from integrating diverse information within a common framework. This is one of the core principles of data spaces: enabling different organizations to share and use information in an interoperable, secure, and governed manner without losing control of their own data.
Third, because it shows that data spaces only become meaningful when they help answer concrete questions. In this case, questions related to metropolitan planning, the location of transport interchanges, modal connectivity, and overall system efficiency.
From Fragmented Mobility to a More Useful View of the Territory
One of the major challenges of today’s mobility systems is that no single actor possesses the complete picture. Transport demand, movements between modes, territorial accessibility, and pressure on key transport hubs cannot be fully understood when each source of information operates independently. This is precisely the type of limitation that projects such as DS4MM aim to overcome.
In the case of the metropolitan pilot, connecting these pieces makes it possible to move toward a more useful understanding of the territory: one that not only describes how people travel, but also helps determine how the network should be strengthened to better respond to real demand.
What Matters Is Not Only the Data, but Its Usefulness
RTCM’s use case captures the fundamental challenge at the heart of DS4MM. The future of mobility will not depend solely on having more information, but on the ability to organize it, share it, interpret it, and turn it into useful decisions.
This is what gives a data space its true value: not centralizing information for its own sake, but creating an environment in which that information can generate value for public administrations, operators, and citizens. And this is where pilots such as this one help bring what can often seem like an abstract concept down to earth: how to move from data to models, and from models to a mobility system that is better planned, better connected, and more efficient.
