Due to the long braking distance of railway systems and the high velocities achieved, railway operation needs to rely on train control systems. At the foundation of these systems are models to predict the motion of the trains, including their anticipated braking curve. Depending on the infrastructure manager, these braking curves need to be achieved with a given safety, which is typically in the rare event region of probabilities. In current settings, it is typical to develop these so called braking curves either by physical modelling of the train followed by a Monte Carlo simulation or following a heuristic approach, mostly based on the high level of safety over the past centuries. However, higher developed train protection and control systems, such as the European Rail Traffic Management System (ERTMS) or the Russian KLUB-U System together with current efforts towards quantitative risk analysis, e.g. the European Common Safety Methods, require a more formal approach to communicate the braking curve of a train between rolling stock and infrastructure. A prior determined set of braking curves is feasible for trains running in fixed or a limited number of formations, such as multiple unit trains, however in the freight railway system due to its vast amount of different vehicles and possible train setups, the determination of the braking curves is prohibitive. This paper develops an approach to obtain braking curves from observed deceleration data, which due to its structure may be applied to data streams of arbitrary size. The proposed algorithm is tested on a simulated set-up.

Pfaff, Raphael*, Elsen, Ingo, Schmidt, Bernd; FH Aachen University of Applied Sciences, Aachen, Germany