How better data from data sharing across (competing) companies can create the data chains required for seamless intermodal travel involving the integration of public transport with micromobility, such as shared bikes, electric scooters, and (autonomous) shuttles, to speed up travel and reduce CO2 emissions.

The problem: Data gap
Climate change requires mobility with less CO2 emissions, and therefore, a modal shift or “Verkehrswende”, from driving our gas guzzlers to using shared mobility, particularly in cities. And as it takes ever longer and is more expensive to get from A to B (for details and metrics, see Schlueter Langdon 2021a), we would certainly entertain switching, if alternatives were better and faster. Unfortunately, there is a data gap: Faster trips require more seamless travel chains (bike-subway-shuttle, for example), which in turn need data chains for optimized connections across different modes and providers of transportation. Today, companies don’t share data, and therefore, no data chains, no travel chains, no seamless intermodal travel.

The solution: Better data with a dataspace
The Telekom Data Intelligence Hub team of Deutsche Telekom’s T-Systems B2B software devision (link) utilized first generation dataspace technology from International Data Spaces Association (IDSA, link) to create a dataspace prototype to enable sharing of data on vehicles (where located?), customers (any memberships?), and services (dockless sharing?). We also created data chains to harmonise different data, such as static data from public transportation (routes and schedules) and dynamic data from free-floating micromobility (creating “data products”, see Schlueter Langdon & Sikora 2020). Furthermore, the traditional routing engine needed to be upgraded to provide travel recommendations based on specific user profiles.

From app fragmentation to “super-app”
Figure 1 illustrates our solution of a super-app for planning end-to-end A to B intermodal trips “powered by a dataspace” (link). It enables the evolution from a hodgepodge of apps on a user’s phone with a myriad of repetitive, time-consuming do-it-yourself trip orchestration steps (app fragmentation on the left of Figure 1) to an all-in-one, one-stop-planning and shopping experience.

Better user experience
How does it work from a customer’s perspective? Figure 2 depicts user interfaces (UIs) for the customer journey and the underlying system architecture (link). To initiate a trip a user starts in the personalized, digital travel twin UI to enter starting point A and destination B as well as perferred starting or arrival time. While the system is calculating route options the UI is switched to a more traditional map view with a widget hovering over it to provide three recommendations either based on a user’s travel history or speed-cost-comfort settings in the digital twin.

Real world test and results
This project was realized as part of the Reallabor Hamburg project (RealLabHH) as a lab of the German Federal Government’s National Platform for Mobility (NPM) with funding from the German Federal Ministry for Digital and Transport. This demonstrator app with dataspace was tested with live data from mobility providers in RealLabHH, including Hamburger Hochbahn AG, Sixt, and Tier Mobility, and at the launch of the system by visitors to the ITS World Congress in Hamburg. The result of better data? Better mobility with 30% faster travel speeds and less CO2 emissions. In 2022, RealLabHH was awarded the “Real Laboratory Innovation Prize” by the Federal Ministry of Economics and Climate Action (link).

Lessons learned: 3 steps – network, data product, super-app
It was useful to decompose the solution into the 3 steps of:

1. Dataspace network (use dataspace for sharing with data sovereignty protection)
2. Data products (harmonize and standardize data chains for ease of adding new travel modes and providers)
3. Super-app (upgrade existing app with agent capability for both data chains and improved user journey).

Our publications … on problem definition … solution design … prototype implementation … real-world testing … and strategic business model implications:

• Announcement: Mobility with IDS – RealLab Hamburg, (Schlueter Langdon 2020, link)
• What: RealLab Hamburg report (RealLabHH 2022, mobility super-app chapter, link)
• Better performance: Faster travel, easier to use (Schlueter Langdon et al. 2021, link)
• How: Customer journeys and agent system (Schlueter Langdon & Eckert 2022, link)
• Business model shift: Selling A to B trips by the seat (Schlueter Langdon 2021b, link)

Our “Auto” miniseries
This piece is the fifth instalment of our “Auto” series. Previous episodes include:

• Auto 1: Digital Service Shift and 7 Gaps (link)
• Auto 2: Power Shift to Data (link)
• Auto 3: One Future of Auto is Airlines (link)
• Auto 4: Auto versus Mobility … Box vs Triangle (link)

References

RealLabHH. 2022. Wir verändern Mobilität – Erkenntnisse des Reallabors Hamburg für eine digitale Mobilität von morgen. Abschlussbericht (2022-04), RealLab Hamburg, Hamburg (super-app chapter only: link)

Schlueter Langdon, C. 2021a. Stuck in traffic: How bad is it … do we age faster … how can we fix it? Telekom Data Intelligence Hub Blog Story, T-Systems, Frankfurt, link

Schlueter Langdon, C. 2021b. Dataspace Enabled Mobility. In: Mertens, C., et al. (eds.). Data Move People. Anthology (version 1.0, October), International Data Spaces Association, Berlin, Germany: 27-41, link

Schlueter Langdon, C. 2020. Mobility with IDS: Adding the “N” in NPM to RealLab HH. IDSA Blog (2020-10-12), International Data Spaces Association, Berlin, link

Schlueter Langdon, C. and Eckert, J. 2022. Intermodal travel super app with agent system and data space: RealLab Hamburg implementation. Scientific Paper ID 1225984, 28th ITS World Congress, Los Angeles, link

Schlueter Langdon, C., N. Oehrlein, and D. Kerinnis. 2021. Integrated Public Transport: Quantifying user benefits – Example of Hamburg. Technical Paper ID 438, 27th ITS World Congress, Hamburg, link

Schlueter Langdon, C., and R. Sikora. 2020. Creating a Data Factory for Data Products. In: Lang, K. R., J. J. Xu et al. (eds). Smart Business: Technology and Data Enabled Innovative Business Models and Practices. Springer Nature, Switzerland: 43-55, link