Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines.

The use of continuous welded rail (CWR) track has solved many of the problems associated with tread surface discontinuities that occur in jointed tracks. However, due to the longitudinal expansion of the rails in CWR tracks being highly constrained, the generated compressive stresses in the rails can cause track buckling in the horizontal plane. Track buckling is a complex phenomenon, in which many factors are involved and around which there is much uncertainty. The objective of this paper is to present an analytical model that can be used to calculate the buckling load of a CWR track. This model accounts for the contributions of base, crib and shoulder ballast and includes the effect of vertical loading on each of these components. Moreover, a parametric study based on this model is developed, in order to understand how and the extent to which the considered factors affect track stability. The results of the study indicate that the characteristics of the existing misalignments in the track are the critical parameters involved in the phenomenon. In addition, maintenance operations that affect the ballast, such as tamping or surfacing, and the dimensions and material of the track sleepers are also important factors.