From data to mobility decisions with greater impact

DS4MM helps public administrations make better decisions based on reliable and comparable data from different sources and regions. The use of mobility models makes it possible to analyze real-world situations and anticipate future scenarios, thereby facilitating more effective planning.

In addition, the project improves coordination among different administrative levels (local, regional, national, and European) and enables the assessment of the impact of public policies, both before and after their implementation, supporting continuous improvement processes.

For operators, it serves as a tool to better understand how demand behaves and how mobility evolves within their area of operation.

The use of models based on real-world data enables:

• Optimizing services and resources.
• Anticipating congestion or demanding spikes.
• Improving operational efficiency and service quality.

All of this contributes to more efficient management and better user experience.

DS4MM offers data providers a secure, regulated, and transparent environment for sharing and reusing the information they generate, without ever relinquishing ownership or control over the data. The project facilitates new forms of public-private collaboration in which data can generate social and economic value under clear rules governing access, use, and governance, thereby strengthening trust among the parties.

Furthermore, DS4MM is conceived as a governed and connected data space, capable of facilitating interoperability not only between organizations but also between different data spaces and institutional ecosystems. This approach addresses an increasingly clear need in the field of mobility: having common frameworks that allow diverse actors to reach agreement without forcing them to relinquish control over their information.

In this regard, DS4MM seeks to overcome the dichotomy between not sharing data and publishing it openly without a defined framework for its use. In contrast to environments where information can be accessed or downloaded without clear conditions regarding licensing, reuse, or liability, the project incorporates mechanisms for governance, access control, and shared responsibility that enable real, reliable, and sustainable collaboration between public and private actors.

For research centers, DS4MM provides a structured environment for accessing interoperable, high-quality data from multiple sources (both public and private), which is essential for the development, training, and validation of advanced mobility models.

Unlike other stakeholders, their role within the ecosystem is not limited to data usage but lies at the methodological core of the project: they define theoretical frameworks, design analytical models, and contribute to the generation and structuring of datasets that are subsequently used by other stakeholders.

This allows for:

• Testing hypotheses against real, multi-source data, ensuring scientific robustness and applicability.
• Developing, calibrating, and validating transportation models and AI algorithms in real-world operational environments.
• Designing replicable and scalable methodologies aligned with the interoperability requirements of the data space.
• Participating in the definition of KPIs and metrics, which are essential for evaluating the performance of use cases.
• Contributing to the generation and curation of datasets, ensuring their quality, consistency, and analytical value.

In this context, universities and research centers such as the UPM and the UPC play a key role by contributing expert knowledge in transportation modeling, data analysis, and mobility planning, as well as by developing their own models that can be integrated and validated within the data space.

As established in the project methodology, research centers actively participate in critical phases such as defining use cases, selecting data, developing models, and validating results, ensuring that the solutions developed are technically sound and aligned with the project’s objectives.

DS4MM thus acts as an effective bridge between research and practical application, enabling the models, methodologies, and scientific advances developed in academia to be tested, validated, and applied in real-world mobility scenarios (regional, metropolitan, and cross-border pilots).

Furthermore, this approach allows for:

• Reducing the gap between research and operations.
• Accelerate knowledge transfer.
• Ensure that models do not remain theoretical but generate real impact.

In short, research centers do not merely consume data in DS4MM; they structure the knowledge that makes it possible to transform that data into real mobility solutions.