Talking about data-driven mobility is relatively simple. The real challenge begins when that data comes from different organisations, uses different formats, has uneven levels of quality and is subject to different conditions of access and use.
Public administrations, transport operators, infrastructure managers, private companies and technology providers generate a huge amount of mobility-related information every day. Traffic data, incidents, routes, timetables, demand, parking, logistics, weather, emissions and travel patterns are just a few examples.
The challenge is to make all this information connect and create value without any organisation losing control over its own data.
That is one of the main goals of DS4MM (Data Space for Mobility Models).
From isolated data to an ecosystem
DS4MM starts from a common situation in the mobility sector: the data exists, but it is spread across many stakeholders, systems and territories.
It is therefore not enough to concentrate information on a single platform. A data space offers a different model: an environment in which different participants can discover, share and use data under common rules, while keeping sovereignty over their own assets.
In DS4MM, this ecosystem brings together six main elements:
- Data sources and owners;
- Governance and rules of use;
- Interoperability and quality mechanisms;
- A common technology platform;
- Mobility models;
- Decision-oriented services or outputs.
The ultimate goal is not simply to exchange information, but to turn data into useful knowledge to plan and operate mobility systems better.
What types of data can be part of the space?
Mobility is a particularly complex ecosystem because it involves sources of very different kinds.
DS4MM covers information on public and private transport (traffic, incidents, routes, timetables, supply and demand), but also data on freight and infrastructure, logistics hubs, stations, ports and airports.
These are joined by users’ travel patterns and flows, urban and territorial information such as Low Emission Zones, parking, charging points and land use, and complementary data such as weather, emissions, geography and events.
Being able to connect these sources makes it possible to build a much more complete picture of mobility than any single dataset can offer on its own.
How does data travel within DS4MM?
The process starts by identifying what information may be useful for a given use case or model. From there, the provider physically keeps the data and publishes the information needed for it to be discovered in the data space catalogue.
When another participant needs to use it, different governance mechanisms come into play: identification of the stakeholders, permissions, traceability and conditions of use. The exchange takes place through secure connectors and usage agreements that allow the provider to keep control over its data.
Finally, that information can feed models capable of generating new results, services or valuable data. In this way, sharing data does not mean giving it up: each organisation can decide who may use it, for what purpose and under what conditions.
An architecture based on trust and interoperability
For different organisations to work within the same ecosystem, their systems must be able to communicate and everyone must interpret information in the same way. That is why DS4MM works on different dimensions of interoperability.
On the one hand, technical interoperability, through connectors, APIs and interfaces that enable secure exchange.
On the other, semantic interoperability, through common vocabularies and glossaries that give the same data a shared meaning regardless of who produces or uses it.
Interoperable metadata and alignment with European standards and frameworks for data spaces also come into play. The aim is for the solution not to work only within a specific project, but to be able to integrate into wider mobility ecosystems.
The building blocks: the pieces that make the data space work
The DS4MM architecture relies on different technology components that work together.
The Identity Manager manages identities, permissions and access.
The catalogue makes it possible to discover the data and services available.
Common vocabularies and glossaries help ensure that producers and consumers interpret information correctly.
Connectors enable the exchange between the two parties while preserving data sovereignty.
The whole ecosystem runs on a cloud infrastructure and a governed architecture that sets the rules, roles and conditions for taking part. In addition, an observability service records events, access, exchanges and possible errors to ensure traceability and make the system easier to audit.
The result is an environment designed around three fundamental principles: interoperability, trust and secure exchange.
From data to mobility models
The value of the data space appears when information stops being an isolated resource and starts being used to build models.
DS4MM includes predictive models that can use historical and real-time data to anticipate behaviour, as well as simulations that represent networks, vehicle flows, demand and supply.
These models can work at different levels of detail (macro, meso, micro or hybrid) depending on the problem to be analysed. They make it possible to obtain forecasts, detect incidents, assess impacts or study different scenarios before taking a decision.
The goal is to move from having data about mobility to using it to understand, simulate and improve it.
Security and sovereignty: sharing without losing control
One of the fundamental principles of DS4MM is that each participant keeps control over its assets.
Data sovereignty makes it possible to set conditions of use and decide which other stakeholders may access certain information. Digital identity shows who is taking part in the ecosystem and what actions they can carry out, while audit mechanisms record access and activity to ensure compliance with the policies in place.
In addition, certain sensitive data does not necessarily have to leave its owner’s environment in order to create value.
This makes it possible to build an ecosystem based on trust, an essential condition for public and private organisations to collaborate and share information.
An infrastructure for real mobility challenges
All this technological development has a practical purpose.
DS4MM aims to apply data spaces to specific mobility problems: congestion, intermodality, emergencies, emissions, logistics, Low Emission Zones and cross-border corridors, among others.
The project connects decentralised data from different organisations under a common framework, sets the rules needed to build trust and uses that information to feed simulation, prediction and assessment models.
At the same time, the work carried out helps to prepare a technical and organisational foundation compatible with future national and European mobility data ecosystems.
Because the real value of a data space does not lie simply in getting information to flow.
It lies in making data that used to be separate work together to help us make better decisions about how we move.
Want to learn more about the architecture, processes and models of DS4MM?
Take a look at the new DS4MM technical presentation, available in the Media Kit section of our website.
