An accurate train motion model is a key component of a wide spectrum of railway applications, from timetabling algorithms to Automatic Train Operation systems. Therefore, model calibration has become crucial in the railway industry, although this topic has not received the attention and recognition in academia that its practical relevance deserves. Several data-driven techniques have been devised to calibrate train dynamics models, although an overview that describes the current state of the art in the field and highlights the following steps to be researched is still missing in the literature. Thus, this article has four main goals. First, giving a brief insight into the broad variety of techniques used for train motion model calibration, focusing on those techniques that use on-board measurements and are applicable in railway operation. Second, highlighting the main research steps to be tackled, considering the current main challenges in railway research. Third, outlining practical recommendations to practitioners who need to calibrate their algorithms and applications. And fourth, contributing to giving train motion model calibration its due recognition.

In the paper the observed parameter variations of a single train into the trajectory
optimization problem is incorporated. The computed trajectories should be energyefficient and
robust with respect to the realized parameter configuration. A new approach for modelling the
Train Trajectory Optimization problem under parametric uncertainty in terms of a minimax
optimal control problem with a finite set of observed parameter configurations presented.