Sustainability, efficiency, and innovation need data, but its availability is limited due to interoperability challenges and fear that the data may be misused. This is why a new approach is being created to enable cross-domain data sharing in a federated and sovereign manner: it is the new paradigm of “data spaces”. This keynote will give an insight on the data spaces approach and will shed light on its current developments and adoption on a European and global scale.

The Autobrennero Group has historically made innovation one of its tools of choice, in line with its strategic mission to guarantee the country the efficiency of its first European link, the Brenner Pass, through which more than 10% of all Italian import-export transits, with an actual number of transits of more than 70 million vehicles. In order to avail itself of the most innovative technologies, first and foremost to increase the level of safety of the infrastructure and reduce accidents, in conjunction with carrying out the functional activities for the management of the motorway artery under concession, the Group has always been involved in research and experimentation projects, availing itself, among others, of the collaboration of numerous Italian and foreign universities. These collaborations have covered the most diverse fields of study and application, from economics to transport, investigating not only safety-related elements, but also the architectural characterisation of the works and the impact of the motorway on the surrounding environment. Increasing motorway capacity without the consumption of additional land and integrating the various forms of transport managed in the best possible way are another priority field of action for Autostrada del Brennero, also because of the particular environmental conditions in which it operates.
Autostrada del Brennero intends to promote the realisation of a European Green Corridor Brenner-Modena that would allow environmentally friendly vehicles to travel the entire stretch between the Brenner Pass and Modena, so as to make the A22 a green, digital and innovative corridor for the mobility of the future.
In connection with sustainable mobility, the Brenner Motorway coordinated from 2016 to 2021 the European project BrennerLEC, co-financed by the European Commission under the LIFE programme. Its aim was to implement experimental traffic control measures to make traffic flows regular, increase motorway capacity and reduce emissions of atmospheric pollutants and climate-altering gases from motorway traffic.
The BrennerLEC project exploited real-time traffic, weather and air quality data from actors in different sectors to generate dynamic traffic management measures, thanks to the availability of the NOI Open Data Hub platform.
On the subject of digitalisation and safety, Autostrada del Brennero SpA has participated in the past and is still participating in investment projects for digital transformation relating to the increasingly pervasive use of ICT in mobility. The objective pursued is to equip the infrastructure with an I2V communication system capable of managing vehicle traffic in the future in a manner not dissimilar to rail traffic, overcoming the current aleatory way of using the infrastructure, in order to increase the capacity of the artery, reduce polluting emissions and progressively eliminate human error, which is today the main cause of accidents.

Traffic data can be an invaluable resource for analysing and tackling the critical issue of air quality. Yet, raw traffic data by itself is not sufficient, and requires tools and technologies for assessing, in a data-driven fashion, the impact of traffic on air quality indicators.
We have developed a cutting-edge tool capable of estimating real-time vehicle traffic emissions from raw traffic data with remarkable accuracy. This tool can be seamlessly integrated into the OpenDataHub platform, giving users easy access to a comprehensive view of traffic data and of the environmental impact of transportation.
Our demonstration of the tool uses traffic data from the Brenner motorway, giving you a look at how the COPERT model (the European standard for vehicle emission calculation) can effectively be used to estimate the levels of primary pollutants and greenhouse gases on the basis of vehicle fleet composition, speed, and vehicular flow.
We will also inspire you with examples and ideas on how this tool can be used by both operators and developers to create value-added services and to enable innovative solutions for decision makers and individual citizens.

Fundamental role of Open Data Hub for green fuel market sizing and scoping and fuel demand analytics in heavy-duty transport across Brenner Pass.

Freely available information on transportation systems, such as schedules, routes, and real-time data allow mobility players to improve transportation services, optimize routes and schedules, and provide better experiences for passengers.

The Open Data Hub is active in two domains (Tourism and Mobility) and is trying to open a new one and provides a large number of data sets. The Open Data Hub offers also different tools (e.g. Data Browser, Analytics, Web Components, etc.) to view the offered data. To help the Open Data Hub users to find the data and the right view they are looking for, the Open Data Hub team decided to implement a new version of the Open Data Hub Data Browser.
During this talk the beta version of the new Open Data Hub Data Browser and the development timeline will be presented.

As Open Data Hub acquires partners and clients, its architecture has to change to adapt to the growing number of requests, the need for a more resilient system, and a more reactive response.
In the past year, Animshon worked alongside the Open Data Hub team to design and build the new core of Open Data Hub. A real-time, event-driven, integrable Open Data Hub.

Open Data is about supporting the insights in between what historically have been inaccessible data, protected by companies or other institutions. When data became more opened, it was possible to see a growing sector of activities exploring the opportunities it created. Business Intelligence, Analytics, and Machine Learning are some names very popular around the subject. This data-driven approach quickly became the key to success, and ideas were put to practice. Despite the popularity, the technical solutions of the software systems were yet naive.

When handling with large amount of data, specially open data, there’s an important lesson learned: systems got to be flexible, as data are. For that matter, Microservices have been widely adopted in systems that handle open data, due to its support for Extensibility. In this architecture, the system is maintained as a set of small, independent parts. Each part, called a microservice, has a specific responsibility, and the different microservices inter-exchange data to meet the system’s and user’s need. In this talk, we’re going through the most indispensable principles and practices when designing computing systems that are data-driven, perfectly tailored for Open Data. We’ll begin providing a perspective on how to establish the communication and shared data between microservices, and evolve into discussing ways to support seamless incorporation of new data-analysis services.

Data validation requires great effort hence it's crucial to build on already existing initiatives, tools and processes. This talk highlights the potential synergy between the work and tools released by the AlpineBits Alliance and the Open Data Hub, two important players in the local community.

Last year we introduced the importance of Data Quality for the implementation of high quality software applications and algorithms and we developed some scripts that analyzed the completeness, validity and accuracy of the Open Data Hub datasets.

The journey in the data quality world did not end there.
Unknowingly, that day was the beginning of a new journey that has one destination: make data quality testing accessible to anyone.

The by-product of this path is a released open source software that gives the possibility of seeing the quality of the dataset based on initially proposed tests and criteria, verify what are the suggested warnings, modify the tests and automate the tests so that they are repeated periodically.

During the talk we will present our journey, what we have learned from it and the first version of the data quality testing software.