By Maximilian Werling, Research Fellow, Ferdinand Steinbeis Institute

Imagine stepping off your flight, effortlessly boarding a driverless rail shuttle, and arriving at your destination without experiencing delays or waiting in queues. This vision isn’t merely a futuristic scenario—it’s increasingly becoming reality, enabled by innovative data-sharing initiatives that integrate diverse information sources to enhance transportation services.

A recent project conducted by the Ferdinand Steinbeis Institute (FSTI) in collaboration with Düsseldorf Airport (DUS) demonstrates that data sharing and digital twins are not simply theoretical concepts, but practical tools capable of transforming mobility ecosystems. This article explores insights from their pioneering transportation testbed at DUS, highlighting how collaborative data-sharing practices are paving the way for the future of transportation.

Digital Twins and the Need for Collaborative Data Sharing

As digitalization reshapes industries, transportation networks are transitioning from isolated systems into highly interconnected ecosystems. Central to this transformation are digital twins—virtual representations of physical assets that enable organizations to monitor, analyze, and optimize operations in near real-time. However, fully realizing the potential of digital twins requires access to comprehensive and diverse datasets, many of which exist beyond the direct control or domain of a single organization. Consequently, seamless data sharing across organizational boundaries emerges as a critical challenge that organizations must address to unlock new levels of efficiency, real-time decision-making capabilities, and operational excellence.

The transportation testbed specifically tackles this challenge. By orchestrating cooperation among multiple stakeholders—including DUS itself, a traffic engineering firm, a transport operator, an analytics provider, and a regional transport association—the project clearly demonstrates how collaborative data-sharing frameworks can substantially enhance passenger experiences and optimize service delivery across the transportation ecosystem.

At the core of the project is the SkyTrain, an autonomous passenger suspension railway system connecting Düsseldorf Airport’s terminals, parking facilities, and nearby long-distance train stations. Due to its driverless operation, the SkyTrain depends significantly on accurate, timely data, making it particularly suited for exploring advanced data-sharing methodologies.

The FSTI spearheaded this initiative, facilitating structured collaboration among diverse stakeholders, each contributing specific expertise and datasets:

• Düsseldorf Airport: Acts as the central coordinator, managing overall operations and integrating passenger flow data.
• Traffic Engineering Company: Responsible for the maintenance and technical operations of the SkyTrain system.
• Transport Company: Operates the nearby long-distance railway station, closely integrated into passenger transit routes.
• Transport Association: Ensures travelers have access to precise, real-time timetable information, crucial for trip planning and seamless transitions.
• Analytics Company: Specializes in analyzing passenger volumes and flow patterns, providing critical insights for optimizing service delivery.
• FSTI: Leads the research and provides scientific oversight and evaluation of the initiative.

Together, these organizations form a collaborative ecosystem designed explicitly to leverage data sharing, generating actionable insights that enhance transportation efficiency and the passenger experience.

Building Trust and Creating a Framework for Collaboration

The effectiveness of collaborative data-sharing initiatives fundamentally depends on mutual trust among stakeholders. Recognizing this crucial factor, the research team prioritized transparency, openly addressing each organization’s motivations and the benefits they anticipated from participating. This transparent approach facilitated trust-building and effectively aligned the interests of all stakeholders involved.

Insights from the testbed’s second phase culminated in the development of a systematic, multi-layered method comprising the following elements:

1. Utility Layer: Explicitly articulated the value propositions for each stakeholder, clarifying the specific benefits each participant would derive from their contributions.
2. Service Layer: Defined the services necessary to achieve the stated benefits. Stakeholders engaged in open brainstorming, deliberately avoiding immediate restrictions related to technical feasibility or existing solutions, to encourage innovative service concepts.
3. Digital Twin Layer: Identified the precise data and information required by digital twins to effectively deliver the envisioned services, with particular emphasis placed on the SkyTrain system and passenger platforms.
4. Data Layer: Structured the specific data points and datasets necessary to support digital twin models, highlighting that these often originate from distinct organizational sources, thereby underlining the necessity of effective data sharing.
5. Asset Layer: Clearly mapped the physical assets and data sources, demonstrating concretely the need for structured, cooperative data-sharing arrangements among participating stakeholders.

By visualizing these layers comprehensively, stakeholders gained clarity not only regarding their individual roles but also on how their data and activities integrate into the broader ecosystem. This structured visualization substantially enhanced stakeholders’ understanding, further solidified mutual trust, and underscored the essential nature of collaborative cooperation.

Implications for Transportation and Beyond

The implications of the testbed extend significantly beyond its immediate context. It illustrates a scalable and adaptable methodology for unlocking the full potential of digital twins and facilitating cross-organizational data sharing. Such an approach is highly relevant and applicable to diverse domains, including urban mobility, logistics, smart city planning, and other complex service-oriented ecosystems.

Specifically, the collaborative model promises several anticipated future benefits:

• Enhanced Real-Time Decision Making: Organizations gain the capability to instantly respond to disruptions, significantly boosting efficiency and improving passenger satisfaction.
• Improved Predictive Maintenance: Digital twins provide precise forecasts of maintenance needs, substantially increasing system reliability, operational uptime, and safety standards.
• Customized Passenger Experiences: Detailed insights derived from passenger flow data enable transportation providers to offer personalized service experiences, tailored to meet evolving traveler expectations and demands.

Looking Ahead: The Future of Collaborative Innovation

Building upon the insights and experiences gathered at the project, researchers at the Ferdinand Steinbeis Institute (FSTI) plan to refine and further develop their methodology. By systematically incorporating lessons learned and extending their approach into additional domains, the research team aims to provide strategic planners and executives with robust frameworks and tools essential for fostering successful, trust-based collaborations in digital ecosystems.

A crucial insight from the testbed is that the digital transformation of transportation—and indeed of numerous other sectors—will not result from isolated initiatives by individual organizations. Instead, true transformation depends on collaborative frameworks where data can be shared seamlessly and securely across organizational boundaries. This collaboration fosters innovative, data-driven services capable of fundamentally reshaping customer experiences.

Thus, the SkyTrain Transportation Testbed stands not merely as an illustrative case study, but as a compelling vision for the future, enabled by mutual trust, strategic collaboration, and shared insights. It demonstrates clearly that when organizations commit to effective cooperation, the possibilities for innovation and impact become virtually limitless.

*Note Feature image by © Andreas Wiese.