Predicting the traction power of metropolitan railway lines using different machine learning models

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Rail Transportation on 2021, available online: http://www.tandfonline.com/10.1080/23248378.2020.1829513[EN] 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.This work was supported by the Ministerio de Economia y Competitividad [TRA2011-26602].Pineda-Jaramillo, J.; Martínez Fernández, P.; Villalba Sanchis, I.; Salvador Zuriaga, P.; Insa Franco, R. (2021). Predicting the traction power of metropolitan railway lines using different machine learning models. International Journal of Rail Transportation. 9(5):461-478. https://doi.org/10.1080/23248378.2020.18295134614789