AI in transport and mobility: advanced driver assistance systems in rail transport

Traction energy consumption is the primary cost factor in the electricity bills of rail transport companies, and is largely determined by how the trains are driven. Energy-efficient speed profiles can therefore lead to significant reductions in power consumption. This includes making the greatest possible use of coasting phases, during which the train consumes no energy. However, it is also vital to coordinate rail traffic with an eye to energy efficiency – for instance by avoiding excessive numbers of simultaneous departures, which result in high peak loads on the electricity grid and therefore additional charges. Moreover, it is important to synchronize arriving and departing trains so that energy recovered while one train is braking can be used to accelerate another.

In the project entitled »Optimization of the subway timetable to minimize energy consumption,« which was carried out in collaboration with the transit provider VAG Verkehrs-Aktiengesellschaft, experts from the ADA Lovelace Center developed optimization procedures to reduce the energy consumption of Nuremberg’s underground rail traffic. The priority here was to make optimum use of the degrees of freedom in timetable preparation – and particularly the ability to reschedule train departures to improve load balancing in the traction network over time. Building on this, the project also included optimization of the trains’ driving behavior in order to reduce overall energy consumption.