Energy savings through optimised operation: Reduction of peak loads in the power grid and more efficient utilisation of energy.
Greater punctuality: Improving timetable stability by supporting punctual departures and optimised passenger changes.
High acceptance among train drivers: User-friendly implementation and promotion of acceptance for smooth integration.
Focal points: Rail transport, increasing efficiency, sustainability, passenger information
Passenger rail transport is already one of the most sustainable mobility options today. And yet there is also great potential here to further increase energy efficiency and improve the punctuality of trains through the use of modern technology. A networked driver assistance system (vFAS), which combines the current timetable situation, infrastructure and driving dynamics, plays a central role here. By exchanging real-time data, driving operations can be optimised so that energy is used more efficiently and peak loads in the power grid are reduced at the same time.
The vFAS was trialled on various routes in the Ostseeküste (OSK) network, including the RE9 (Rostock - Stralsund - Saßnitz/Binz), RE10 (Rostock - Stralsund - Pasewalk) and RE8 Nord (Wismar - Berlin-Spandau) lines. The project participants deliberately chose these routes as they have different infrastructure and operating conditions - from urban junctions to rural sections.
The system was introduced in three phases:
Data collection: Initially, driving data was collected as a basis for comparison without the system making any recommendations.
Driving recommendations for individual train journeys: In the second phase, the system provided initial recommendations for the optimum speed. Individual trains were taken into account in the calculation.
Overall optimisation for networked train journeys: In the final phase, the journey data of several trains was taken into account simultaneously, so that an even more precise calculation of the optimum speeds and a targeted adjustment of the total energy consumption was possible.
Increased efficiency through more precise calculation of the optimum driving style
A central aim of the project was to optimise energy consumption in the power grid and avoid peak loads. Analysing the driving data and simulating train journeys showed that targeted driving recommendations could avoid high energy consumption, especially in the short term. A moderate change in speed or stopping time was the most effective, as excessive changes in driving behaviour could affect the punctuality of the trains. In addition, the researchers developed a model that made it possible to predict the duration of passenger changes more accurately. This information helped to make better use of timetable reserves and to organise operations more efficiently.
Acceptance in test operation: successful involvement of train drivers
It was clear from the outset that the acceptance of new technologies is crucial to their success. The project therefore placed great emphasis on involving train drivers. Through interviews, questionnaires and the evaluation of driving data, the project participants checked how well the system was accepted by the users. The results showed that the vFAS contributed to a more energy-efficient driving style and that the train drivers found the early involvement of their experiences and wishes in the development process to be positive. The vFAS was implemented as a smartphone or tablet app and equipped with a user-friendly interface (see Figure 1).
Figure 1: The vFAS user interface including the display of a driving recommendation.
Optimising passenger information: a key to more efficient processes
In addition to supporting train drivers, the project also focussed on improving passenger information. Using observations on the platform, the researchers investigated which additional information could be helpful for passengers. Using simulations in virtual reality, various options for providing this information were then visualised and compared by study participants in terms of their usefulness. The display of train capacity utilisation (see Figure 2) and the marking of a train's boarding and alighting doors proved to be helpful. This could speed up passenger changes and make operations more efficient.
Figure 2: Example of a train utilisation display in the simulated VR environment.
Conclusion: A step towards sustainable and efficient rail transport
The tests and evaluations of the FASaN project confirm the potential of a networked driver assistance system to optimise rail transport. Important progress can be made by reducing peak loads and saving energy. Another success was the implementation of the driving recommendation by the train drivers and the resulting more energy-efficient driving style, which forms the basis for a successful introduction and expansion of the technology to other routes and vehicle types. This is an important step towards a resource-conserving and more sustainable rail transport of the future.
