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

J. Pineda-Jaramillo, P. Martínez-Fernández, I. Villalba-Sanchis, P. Salvador-Zuriaga and R. Insa-Franco

International Journal of Rail Transportation, 2021, vol. 9, issue 5, 461-478

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/23248378.2020.1829513 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjrtxx:v:9:y:2021:i:5:p:461-478

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjrt20

DOI: 10.1080/23248378.2020.1829513

Access Statistics for this article

International Journal of Rail Transportation is currently edited by Wanming Zhai and Kelvin C. P. Wang

More articles in International Journal of Rail Transportation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().