Micro mobility has emerged as a key factor in urban transportation systems, providing flexible and sustainable alternatives for short-distance travel. Electric scooters, bikes, and other zero-emission vehicles not only complement traditional modes of transportation but also reshape urban mobility, creating a more seamless and intelligent way of getting around.

Flexible Options for Short Trips

Micro mobility offers urban dwellers a convenient way to navigate the hustle and bustle of the city, especially for short-distance trips. Electric scooters and bikes are quick and user-friendly, facilitating effortless transitions from one point to another. This makes them an ideal solution for addressing the last-mile challenge, transforming the journey from public transportation to the final destination into a smooth experience.

Seamless Mobility in Congested Areas

The congested areas of urban centers often pose challenges for transportation, but micro mobility provides a solution where vehicles can move quickly and flexibly through traffic, bypassing congestion and efficiently reaching their destinations. This speed and flexibility make micro mobility an invaluable part of urban transportation.

Key Role in Seamless Urban Mobility

Helsinki is a pioneer in discovering and implementing new technological solutions and actively shaping the future of urban transportation, considering micro mobility and its integration into the city's transportation systems. Mobility Lab Helsinki is playing a crucial role helping companies with real users in this evolution by testing an AI and computer vision solution in traditional sensing and drones for analyzing transport modes and identifying infrastructure elements understanding micro mobility within urban environments and Digital Twin. The use of artificial intelligence and computer vision enables contributing to the development of safer and more seamless transportation solutions.

Environmental Friendliness and Sustainability

Micro mobility is not only convenient but also an environmentally friendly option. Electrically powered vehicles reduce transportation emissions, promoting sustainable urban transportation. Smaller carbon footprints make micro mobility an attractive choice for those who want to positively impact the environment.

The Future of Mobility

Evolving technologies, such as smart sensors and AI, offer more opportunities to enhance the usability and safety of these zero-emission vehicles. Collaboration with traditional transportation systems can create a harmonious urban environment where different modes of transportation work seamlessly together. The broad adoption of micro mobility in cities, combined with new initiatives indicates that the future of mobility is diverse, intelligent, and continually evolving. Zero-emission vehicles are not just a trend; they play a crucial role in the development of urban transportation towards a more seamless and sustainable future.

R&I Decentralised data spaces

Decentralized data spaces that facilitate real-time data sharing among ecosystem operators and mobility providers are essential in addressing the complex challenges of urban mobility and data sharing. These data spaces provide a foundation for innovation, efficiency, sustainability, and safe and efficient information gathering. The SEDIMARK use case pilot explores the mobility digital twin concept to lay the groundwork for future building blocks in secure decentralized mobility data spaces in urban environments.

References

• Mobility Lab - projects https://mobilitylab.hel.fi/projects/
• The ACUMEN project promotes seamless mobility using an AI tool - https://forumvirium.fi/en/projects/the-acumen-project-promotes-seamless-mobility-using-an-ai-tool/

As inferred by the project acronym itself, SEDIMARK is a data-driven project, joining the global trend where individuals and companies produce tons of data on a daily basis.

The initial impulse in any initiative of this type involves focusing on the purely operational and organizational aspect. However, if we stop to think about the treatment carried out on these generated and collected data, the imperative need to dedicate space to the ethical section of its manipulation falls under its own weight.

Hence, the European Commission recommends for this kind of project to check periodically potential ethical issues. First, to make sure that the project does not use personal data (or if it happens to use that this is in compliance with GDPR and other relevant requirements) and second because this specific project will use AI-based solutions and thus it is important to make sure that they are in line with the standards in this area.

What does an Ethics Board do?

Attention to these ethical issues should not fall exclusively on the members of the consortium, who are ultimately interested parties in the process. That is why it is advisable to have opinions from voices external to the project, with proven experience in the project's work areas, capable of evaluating with a critical and well-trained eye the work carried out by SEDIMARK to preserve an ethical approach. These people make up the so called ethics board.

In the context of SEDIMARK, such Ethics Board will proactively support the development of the project, act as a knowledge and guidance forum, and provide advice to the consortium on how to exploit knowledge created by the project.

Hence, SEDIMARK partners will invite the Ethics Board members on a regular basis to discuss about ethical aspects taking into account the project evolution, keeping them well informed of all relevant publications and/or events SEDIMARK-related.

Specific goals for SEDIMARK’s Ethics Board

The purpose of every Ethics Board Member participation is to contribute to a series of objectives through the provision of assessment to the project consortium on topics such as:

• Help validate and provide feedback to the technical designs of SEDIMARK.
• Assist the consortium in the elaboration of ethical-based reports, with a strong emphasis on personal data, AI and ethical incentivization of data sharing.
• Active participation in project conferences, workshops and meetings that require of their participation.
• Participate in discussions on open topics of your interest and for SEDIMARK:
  • give specific opinions,
  • seek alignment and convergence with other initiatives,
  • the adoption of standards in the sector and the reuse of previous work,
  • help maximise the benefits for a wide spectrum of stakeholders.

For all of the above, Ethics Board members will act as advisors in order to follow ethical issues, based on the report that the consortium will prepare on a yearly basis. Such report specifically focuses on explaining data protection issues (if any) and compliance with AI ethical standards. Its initial iteration was already issued during the first year of the project execution and will be a subject for updates along the way.

#Ethics #AI #DataSharing

This deliverable is a first version of the deliverable named “Edge data processing and service certification”.

The work has focused on identifying the building blocks required to develop a framework for deploying AI-based data processing and sharing modules at edge data sources, considering edge-cloud interactions following MLOps principles. It involves analysing various ML frameworks such as TensorFlow, PyTorch, tinyML, and edgeML, while addressing security and privacy concerns by implementing adaptive edge anonymization or tagging of sensitive data.

At this stage of the project these topics are still quite exploratory; this report focuses thus more on challenges, considered options and possible implementation choices. The last version of this deliverable, which is due M34 (July 2025), will provide all the details about the technical choices and their implementation.

This document, along with all the public deliverables and documents produced by SEDIMARK, can be found in the Publications & Resources section.

SEDIMARK D3.3 “Enabling tools for data interoperability, distributed data storage and training distributed AI models” is a report produced by the SEDIMARK project. It aims at providing insights in relation to progress made for:

• Management of interoperability in data flows by proper definition and usage of metadata and semantic technologies
• Distributed storage considering interoperability and scalability issues of such storages.
• Finally, on the use of distributed AI and analytics, considering in particular federated learning.

Data quality is considered to be of the highest importance for companies to improve their decision-making systems and the efficiency of their products. In this current data-driven era, it is important to understand the effect that “dirty” or low-quality data (i.e., data that is inaccurate, incomplete, inconsistent, or contains errors) can have on a business. Manual data cleaning is the common way to process data, accounting for more than 50% of the time of knowledge workers. SEDIMARK acknowledges the importance of data quality for both sharing and using/analysing data to extract knowledge and information for decision-making processes.

Common types of dirty or low-quality data include:

1. Missing Data: Incomplete datasets where certain values are not recorded.
2. Inaccurate Data: Data that contains errors, inaccuracies, or typos. This can happen due to manual data entry errors or system malfunctions.
3. Inconsistent Data: Data that is inconsistent across different sources or within the same dataset. For example, the same entity may be represented in different ways (e.g., "Mr. Smith" vs. "Smith, John").
4. Duplicate Data: Repetition of the same data in a dataset, which can distort analyses and lead to incorrect results.
5. Outliers: Data points that deviate significantly from the majority of the dataset, potentially skewing the analysis.
6. Bias: Data that reflects systematic errors due to a particular group's overrepresentation or underrepresentation in the dataset.
7. Unstructured Data: Data that lacks a predefined data model or organization, making it difficult to analyse.

Thus, one of the main work items of SEDIMARK is to develop a usable data processing pipeline that assesses and improves the quality of data generated and shared by the SEDIMARK data providers.

This document, along with all the public deliverables and documents produced by SEDIMARK, can be found in the Publications & Resources section.

"Much as steam engines energised the Industrial Age, recommendation engines are the prime movers digitally driving 21st-century advice worldwide"

Recommender systems are the prime technology for improving business in the digital world. The list is endless, from movie recommendations on Netflix to video recommendations on YouTube, to playlist recommendations on Spotify, to best route recommendations in Google Maps.

Recommender systems are a subset of Artificial Tools designed to learn user preferences from the vast amount of data, including but not limited to user-item interactions, past user behaviours, and so on. They are capable of delivering individualised, tailor-made recommendations that enhance user experience while boosting business revenue.

In SEDIMARK our goal is not only to provide a secure data-sharing platform, but also to enhance the experience of our users through the use of Recommender Systems at multiple levels:

• Navigating the vast amount of datasets available within the platform
• Suggesting ready-made AI models relevant to the given dataset
• Suggesting computational services capable of handling the given dataset

Recommending Datasets:

SEDIMARK is a platform offering a vast amounts of data available for purchase in a secure way. To enhance the user experience, the team at UCD is developing cutting-edge recommender systems specifically targeted at dataset recommendation. The system will leverage data from past users’ behaviour such as past purchases, past browsing history, or behaviour of other similar users.

Recommending Ready-made AI Models:

Apart from datasets, SEDIMARK will also offer ready-made AI models capable of extracting information from the specific dataset, such as for example weather forecast AI model that can learn from weather data collected by sensors. The recommender system in this category will be able to suggest the most relevant AI models for the given dataset. This will allow the users to fully explore the potential of the purchased dataset within the SEDIMARK platform.

Recommending Computational Services:

Depending on the size of the purchased dataset and the complexity of the AI model, SEDIMARK will also aim to suggest the appropriate computation services that can carry out the learning process and are available within the SEDIMARK platfrom. 

Using a single platform, users in SEDIMARK will be able to perform data discovery, learn insights from a given dataset using AI models and utilise computational services.

In the strive to achieve interoperability in information models, it is important to relate concepts that we define in our models to others that have already been developed and have gained popularity. This can be done by reuse, inheritance or an explicit relationship with that concept. There are many approaches to ontology development, but by far, Protégé has been the de facto tool. Reusing concepts from another ontology in Protégé involves importing the relevant axioms from the source ontology into your current ontology. Here is a quick tutorial on how to achieve this:

1. Open your ontology

Start Protege and open the ontology where you want to reuse concepts.

2. Open the reused ontology in the same window

3. Reuse specific concepts

Protégé provides tools like ‘Refactor’ to copy or move axioms between ontologies.

You can select specific classes, properties, and axioms to import into your ontology.

Select Axioms by reference, which will include other relationship with respect to the concept to be reused.

Select the concepts required:

Check that the other axioms to be included are relevant.

Select your ontology as the target.

4. Manage Namespaces

Ensure that the namespaces for the reused concepts are correctly managed in your ontology to avoid conflicts.

5. Save Changes

After reusing the desired concepts, save your ontology to apply the changes.

To note, reusing axioms from another ontology may result in new logical consequences, therefore consistency and correctness of the developed ontology should be validated after the reuse process.

This report represents the first version of the SEDIMARK’s approach on what will be its data sharing platform, as the main entry point to the system from the outside world. Hence, it must touch base not only on the front-end users will interact with but also on added features such as the Recommender system and the Open Data enabler which are at the essence of the solution. Given the stage on the project execution, the contents hereby presented will be subjected to an evolution and thus a new version of the SEDIMARK data sharing platform will be provided in Month 34 (July 2025) in the Deliverable 4.6 (Data sharing platform and incentives. Final version). Therefore, this document does not offer a fully functional depiction of this platform, just a high-level presentation of its constitutive components instead. In fact, in what refers to the Marketplace front-end a description will appear in the report, while as only the Recommender system and the open data enabler will be described also from a backend perspective. Thus, it is intended for a certain audience, mainly for members in the project consortium to employ it as the template to drive specific technical activities from other work packages within SEDIMARK.

This document, along with all the public deliverables and documents produced by SEDIMARK, can be found in the Publications & Resources section.

In response to the growing demand for secure and transparent data exchange, the infrastructure of SEDIMARK Marketplace leverages cutting-edge technologies to establish a resilient network.

This is the first version of the decentralised infrastructure and access management mechanisms. This deliverable presents a comprehensive overview of the decentralized infrastructure and access management mechanisms implemented in the SEDIMARK Marketplace. As the landscape of data exchange evolves, the decentralization approach ensures increased security, transparency, and user-centric control both over data assets and user identity information. Data providers of the SEDIMARK Marketplace can also provide additional types of assets, related to their data. Examples are Machine Learning (ML) Models, data processing pipelines and tools.

Operating within the principles of decentralization, this project addresses the growing need for secure and transparent data exchange in a globalized digital economy. The SEDIMARK Marketplace leverages distributed ledger technologies to establish a resilient and scalable infrastructure. The decentralized architecture of the marketplace is built on a robust distributed ledger employed for user identity management, as well as blockchain foundation, fostering tamper-resistant contracts. Utilizing a distributed network, the infrastructure eliminates single points of failure, enhancing reliability and ensuring the continued availability of assets to be exchanged. The decentralized infrastructure supports standardized protocols for data exchange, enabling collaboration and data sharing across various platforms and participants.

This deliverable is the first capstone in the SEDIMARK project, realizing the underlying infrastructure and mechanism that allow the fulfilment of the functionalities defined for the Marketplace. An updated version of this deliverable will be provided in the next Deliverable SEDIMARK_D4.2 in July 2025.

This document, along with all the public deliverables and documents produced by SEDIMARK, can be found in the Publications & Resources section.

This document is the first deliverable from WP3, aiming to provide a first draft of the SEDIMARK data quality pipeline. This document details how the pipeline aims to improve the quality of datasets shared through the marketplace while also addressing the problem of energy efficiency in the data value chain. This document is actually the first version of the deliverable, showing the initial ideas and initial implementation of the respective tools and techniques. An updated version of the deliverable with the final version of the data quality pipeline will be delivered in M34 (July 2025). This means that the document should be considered a “live” document that will be continuously updated and improved as the technical development of the data quality tools evolves.

The main goal of this document is to discuss how data quality is seen in SEDIMARK, what are the metrics defined in order to assess the quality of data that are generated by data providers, and what techniques will be provided to them for improving the quality of their data before sharing on the data marketplace. This will help the data providers to both optimise their decision-making systems for the Machine Learning (ML) models they train using their datasets, and to increase their revenues by selling datasets of higher quality and thus higher value. Regarding the first argument, it is well documented that low-quality data has a significant impact on business, with reports showing a yearly cost of around 3 Trillion USD, and that knowledge workers waste 50% of their time searching for and correcting dirty data [1]. It is evident that data providers will hugely benefit from automated tools to help them improve their data quality, either without any human involvement or with minimum human intervention and configuration. 

The document presents high-level descriptions of the concepts and tools developed for the data quality pipeline and the energy efficiency methods for reducing its environmental cost, as well as concrete technical details about the implementation of those tools. Thus, it can be considered that this is both a high-level and a technical document, thus targeting a wide audience. Primarily, the document targets the SEDIMARK consortium, discussing the technical implementations and the initial ideas about them, so that the rest of the technical tasks can draw ideas about the integration of all the components into a single SEDIMARK platform. Apart from that, this document also targets the scientific and research community, since it presents new ideas about data quality and how the developed tools can help researchers and scientists improve the quality of the data they use in their research or applications. Similarly, the industrial community can leverage the project tools to improve the quality of their datasets or also assess how they can exploit the results about energy efficiency to reduce the energy consumption of their data processing pipelines. Moreover, EU initiatives and other research projects should consider the contents of the deliverable in order to derive common concepts about data quality and reducing energy consumption in data pipelines.

This document, along with all the public deliverables and documents produced by SEDIMARK, can be found in the Publications & Resources section.