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Dieses Kapitel skizziert eine Methodik für die Erstellung und den Betrieb von Mobilitätsdatenlabors, wobei der Schwerpunkt auf Zusammenarbeit, Datenaustausch und Innovation liegt. Der Ansatz integriert das Vierfach-Helix-Modell und das Plan-Do-Check-Act-Tool (PDCA) und betont nutzerzentrierte Innovation. Die Methodik wird durch das MobiDataLab-Projekt demonstriert, an dem Stakeholder aus verschiedenen Sektoren beteiligt sind und das den PDCA-Zyklus als Leitfaden für das Projekt verwendet. Das Projekt unterstreicht auch die Bedeutung der FAIR-Prinzipien für die Verbesserung der Wirksamkeit von Mobilitätsdienstleistungen. Das Kapitel schließt mit einer Reflexion über den Erfolg der Methode und ihr Potenzial für zukünftige Fortschritte im Bereich der Mobilitätsdatenlabors.
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Abstract
Data plays a pivotal role in modern mobility research, and data labs have emerged as powerful platforms for promoting collaboration, innovation, and data sharing in the mobility sector. This paper presents a comprehensive methodology designed to plan and execute data labs in the mobility sector, along with its successful implementation within the EU-funded MobiDataLab project context. Central to the methodology is the identification of critical stakeholders, the precise delineation of objectives and challenges, ensuring alignment with genuine mobility issues. It staunchly adheres to a user-centric approach, actively engaging end-users, entrepreneurs, developers, researchers, start-ups, and SMEs with data expertise through a systematic, replicable strategy. Additionally, this paper seeks to introduce the technical infrastructure that was designed and deployed to facilitate these endeavors. The methodology emphasizes the establishment of robust data management systems, protocols, and privacy safeguards. Simultaneously, the methodology advocates for the promotion of interoperability standards and open data formats to facilitate seamless access to diverse data sources. Advanced open data catalogues and data enrichment processors, with anonymization features, further enhance data collaboration and privacy. Capacity-building initiatives enhance stakeholder skills, supported by an Open Knowledge Base for sharing best practices. The methodology's efficacy is finally illustrated through case studies which underscore the concrete benefits of data labs in advancing formal innovation and collaboration within the mobility sector.
1 Introduction
In the rapidly evolving landscape of mobility solutions, harnessing the power of data has become the cornerstone for driving innovation, enhancing user experiences, and making transport systems more efficient and sustainable. Data labs have emerged as dynamic hubs where stakeholders from various domains converge to co-create, experiment, and leverage data-driven insights for mobility solutions. This paper outlines a robust methodology for orchestrating the creation and operation of mobility data labs, fostering collaboration among diverse stakeholders, facilitating data sharing, and igniting innovation.
In the upcoming sections, we will delve into the core elements of our methodology. Within the methodology section, we emphasize the significance of a user-centric approach and delve into how we seamlessly integrate the quadruple helix model and the Plan-Do-Check-Act (PDCA) tool. As we progress, the implementation in the MobiDataLab project section provides concrete evidence of our methodology's success. Here, we explore the ambitious goals of the MobiDataLab project, its holistic design principles, and the indispensable role played by stakeholders in propelling innovation forward. Concluding our exploration, we delve into the crucial role of FAIR principles in enhancing the efficacy of mobility services, detailing how the MobiDataLab project has successfully integrated these principles into its framework. Finally, through reflection and iterative refinement, we have forged a methodology that paves the way for progress in the realm of mobility data labs.
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2 Methodology
To embark on the journey of establishing and operating mobility data labs, it is paramount to establish a user-center methodology that not only leverages the expertise of diverse stakeholders, but also prioritizes the real-world needs and challenges of the mobility sector. This paper, therefore, seeks to provide real-world evidence and insights into the establishment and operation of mobility labs based on the findings of the EU MobiDataLab research project.
2.1 Living Labs and the Quadruple Helix Model
The Living Lab approach, along with its adaptations like the mobility labs, operates on the concept of the quadruple helix model of innovation collaboration. In this model, government, industry, academia and the general public join forces, collaboratively generating innovative solutions [1]. This approach aligns seamlessly with the mobility data lab framework, as it actively involves these key stakeholders, promoting a holistic approach to mobility innovation. Schaffers et al. (2007) underscores the significance and promise of engaging (end) users actively and consistently throughout the development process, through a collaborative co-creation approach with the developers [2]. The shift in living labs towards emphasizing early-stage innovation like user research and co-creation holds promise for enhancing user participation in ICT innovation and unlocking their innovative potential [3].
2.2 Beyond the State of the Art
As a notable advancement beyond conventional practices, the MobiDataLab project has forged a groundbreaking methodology for the inception and execution of mobility data labs. This innovative approach seamlessly integrates the well-established Plan Do Check Act (PDCA) tool, bolstering it with invaluable insights distilled from the iterative experiences of MobiDataLab living labs. These lessons learned from real-world iterations have profoundly enriched our methodology, elevating it to a new standard of effectiveness and practicality.
The PDCA cycle involves planning, executing, evaluating, and refining solutions iteratively. It ensures that innovations are not only ideated but also rigorously tested, refined, and aligned with real-world challenges. This methodology, rooted in the living lab concept, takes innovation a step further by actively involving a diverse range of stakeholders, including end-users, in each phase of the PDCA cycle (Fig. 1).
The MobiDataLab uses the PDCA approach to guide the project stakeholders: project partners (10 project partners from industry, research, academia, consultancy and governance sectors, located in 7 countries), the reference group, the living labs attendees and external participants to pave the way for successful mobility data sharing.
The execution of the approach did start with an overview on the state-of-the-art, the implementation of the transport cloud, the execution together with the stakeholder group within the three living labs (the Datathon, the Hackathon and the Codagon) and the review of domain expert along the path to reflect and integrate the feedback.
The efficacy of our methodology is rooted in its ability to actively involve stakeholders across the mobility spectrum. Entrepreneurs, developers, researchers, start-ups, and SMEs, armed with data knowledge, become essential participants, equipped to tackle the challenges posed by data providers. Figure 2 shows the five stakeholder groups with their motives and needs as a result of a comprehensive stakeholders’ analysis within the mobility sector, coupled with an extensive literature review of successful implementations of mobility living labs.
End-users who use mobility services (e.g. commuters, residents, and citizens) and indirectly affected population groups.
The Virtual Lab platform, a significant component developed within the MobiDataLab project, plays a pivotal role in the realisation of the project’s mobility labs. Virtual Lab is a digital counterpart to the traditional living lab concept and contains various functionalities that support discussion and promotion of solutions throughout their inception life cycle, from challenge to idea to a prototype [5].
4 FAIR Data and Services
Effective open innovation hinges on the accessibility and availability of open data. As a result of the project and improved by the living labs, the Virtual Lab platform is following the FAIR principles (findable, accessible, interoperable and reusable) [6] and provides access to mobility metadata catalogues and a service catalogue. The practical application of these catalogues is exemplified through the various challenges tackled during the MobiDataLab Living Labs, demonstrating the platform's ability to address real-world mobility issues and catalyze innovative solutions1.
In the context of the data labs, it is necessary to make mobility data easier to find, discover and reuse. This is achieved using data catalogues, which enable a more efficient search on the key aspects of each dataset, i.e. descriptive metadata. In the open data ecosystem, a number of software systems can serve this purpose, for example CKAN, GeoNetwork, OpenDataSoft, Socrata [7]. The first difficulty is choosing a solution. In MobiDataLab, the CKAN solution has been elicited due to its modularity, the number of available extensions, and their variety. In particular, the CKAN harvesting extension functionality was implemented, allowing to add datasets on demand into the MobiDataLab data catalogue, from data portals corresponding to the local area of referring municipalities. A future challenge for the MobiDataLab platform is overcoming organisational mistrust, rooted in fears of granting competitors an unintended advantage. Usability by data consumers across the European union remains a challenge since European citizens speak different languages. Harvesting metadata in the local language and translating them in real time into the consumer or most spoken language requested the implementation of dedicated extensions, resulting in MobiDataLab’s contribution to open-source software. Not only the MobiDataLab platform improves findability, but also reusability, offering anonymization techniques to data providers, such as microaggregation for anonymising large mobility datasets [8].
Today’s mobility services are very diverse and distributed. The FAIR principle is less established and implemented. Catalogues to register services and make them findable are becoming more requested but are not yet stablished. Also, standards for interoperable interfaces are not available or still in discussion. Within the MobiDataLab project, a catalogue with an easy interface to register OpenAPI (https://www.openapis.org/) described services is part of the Transport Cloud ecosystem. The catalogue allows the registration of REST APIs and makes them findable. Further demands are easy access, interoperable interfaces (e.g. aligned and agreed by service providers) to fulfill the reusability as the fourth step to make mobility services FAIR.
5 Conclusion
The methodology we present here is a blueprint for harnessing the full potential of data labs in the mobility sector. By bringing diverse stakeholders to the table, setting clear objectives, and fostering user-centric innovation, we enable the co-creation of solutions that address the pressing mobility challenges of our time. The PDCA approach implements the agile principle and aligns and supports all stakeholders on the way to a FAIR approach for mobility data sharing. The provisioning of data and services and connection of data producers and data and service consumers in the context of the Living and Virtual Labs improves the alignment and enables the connectivity between stakeholders through communication and collaborative solution enablement.
The MobiDataLab project stands as a testament to the effectiveness of this methodology, demonstrating that by working together, we can create a future of more efficient, sustainable, and inclusive mobility solutions.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
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