Changing public transport for the better


Challenge identifier: DPC4-2017


Proposed by

Deutsche Bahn AG is a largest railway operator and infrastructure owner in Europe and the second-largest transport company in the world.  It carries about two billion passengers each year and operates, via its subdivisions, in 12 European countries.


Public transport impacts on everyone’s standard of living. It increases sustainability, accessibility, productivity and revenues and reduces carbon emissions, congestion and air pollution. The way people move around – especially in urban areas – is on the cusp of radical change. New business models around the so-called ‘sharing economy’ are disrupting traditional mobility patterns. Technical advances such as electric, connected and autonomous vehicles are not just science fiction anymore. Much of this wave of innovation rests on the Internet and smart apps, such as driverless buses or traffic lights programmed to keep trucks from obstructing the overall flow; and on new types of engines, which make bicycles, cars, trains and buses more eco-friendly.

Deutsche Bahn is constantly improving their customer services via digital solutions, enhanced business operations and new business models. In this context, Deutsche Bahn is looking for ideas of how data can change the future of mobility, across different areas: products and services leveraging Deutsche Bahn data that cater every aspect of Deutsche Bahn’s operations; data value chain technology; and business models enabled by access to more and richer data.

As a strong advocate of open innovation, Deutsche Bahn also welcomes ideas and business cases based on existing prototypes, simulations and mock-ups that convincingly demonstrate the value of Deutsche Bahn datasets that are currently not published openly. This data will be shared with the challenge winners.


Deutsche Bahn is offering access and support in using a range of static and stream data, including

  • Master data: rail network; stations (addresses, GPS, length of platforms etc.); opening times travel centers; operations location register (RIL 100/DS 100); service facilities.
  • Business data: 2.5 years worth of booking data from Call-a-bike and Flinkster (anonymized); network radar (availability of mobile networks from app measurements); air pollution registry.
  • Logistics data: samples of shipment data for 8 containers around the world from DB Schenker (location, temperature); DB Cargo data (aggregated per 10 trains per operating location per day).
  • Target data: target data car position diagram/indicator; target timetables Fernverkehr (long-distance trains).
  • Real-time APIs: condition of elevators and escalators & master data (following the ADAM-API); booking options for Call-a-bike and Flinkster; actual occupancy of DB Bahnpark car parks; APIs to master data (see above).

Applications may decide to provide solutions that leverage this data in combination with open datasets released by other transport companies in Europe.

More detailed information about the data can be found in our data catalogue. 


Expected outcomes

  • Apps and services that analyse customer needs and improve transport experience across different dimensions, from costs to efficiencies to environment.
  • Novel algorithms offering comprehensive analytics, reporting, and visualisations of the data, going beyond the mere application of existing technologies to new datasets.
  • Transformative, data-driven business models, tested in relevant conditions.

Expected impacts

  • New business ideas and services, improving the quality of the mobility experience and creating substantial business efficiencies for the Deutsche Bahn.
  • New digital business ideas for startups or existing transport businesses in Europe.