Energy-efficient timetable optimization for the Nuremberg subway

Traction energy consumption, i.e. the electricity used to power the trains, is the most important cost factor in the electricity bill of a rail transport company. It is primarily determined by the way the trains are driven. However, energy-efficient coordination of train traffic can also make a decisive contribution to reducing costs through its potential for energy recovery. In the project, experts in mathematical optimization from Friedrich-Alexander-Universität Erlangen-Nürnberg and the Fraunhofer Institute for Integrated Circuits IIS worked together with VAG Verkehrs-Aktiengesellschaft to reduce the energy consumption of the Nuremberg subway railroad by means of an efficient driving style in conjunction with intelligent coordination of all trains.

The methods used here are based on an earlier research project at FAU (E-Motion), in which the question was investigated as to whether the peak power requirements in the traction current network could be reduced by making minor adjustments to the timetable. The aim was to identify potential for reducing electricity supply costs. To this end, suitable mathematical optimization models were created, analysed and solved. These are based on so-called clique problems in graphs, as which the schedules to be found can be coded. This previous project resulted in a tailor-made, prototype software implementation that was able to output energy-optimized timetables. In fact, it could be shown that significant reductions in peak load were possible with slight shifts in the departure times of the trains (by up to 3 minutes).

This successful work on the underlying mathematical, graph-theoretical issues has made the ADA Lovelace Center and developed in the project »AI in transport an mobility« the methods further. A deeper basic understanding of the mathematical-abstract task enables projects with industry to build on this, for example to look at even more complex timetable issues and to solve related tasks, e.g. from the production planning of an industrial company.

Braking and acceleration processes can be controlled very precisely, especially for automatic trains such as the Nuremberg subway. To this end, the experts developed software that uses innovative algorithmic methods of mathematical optimization to optimally align the given timetable design in terms of energy consumption by readjusting the departure and travel times. For this purpose, existing real traffic and consumption data was used, evaluated and transferred into optimization models. The optimization models developed show a significant potential for savings in energy consumption, which will pay off for VAG in significantly lower electricity costs. Under optimal conditions, a significant reduction in consumption is possible.

The mathematical methods developed can also be applied to the maintenance management of trains. The role of the limited available resource whose use is to be optimized is assumed by the factor of personnel and machine deployment instead of energy. However, the technology developed here is also of interest to other sectors: In industrial manufacturing, for example, every large manufacturing company is priced by the electricity provider for its peak electricity requirements in addition to its total energy consumption. Optimized load management, i.e. optimal timing of machine use, can also save a significant amount of electricity costs here. This opens up a wide range of possibilities for utilizing the results.

The Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and the Fraunhofer Institute for Integrated Circuits IIS worked together with VAG Verkehrs-Aktiengesellschaft Nürnberg, a subsidiary of Städtische Werke Nürnberg, on the project »Energy-efficient timetable optimization in Nuremberg's subway transport system« to reduce the power consumption of the Nuremberg subway system. The two institutions are pooling their expertise in the development of mathematical optimization algorithms and their implementation in practice.