Real-time train motion parameter estimation using an Unscented Kalman Filter

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Alex Cunillera - , Technische Universität Delft (Autor:in)
  • Nikola Bešinović - , Technische Universität Delft (Autor:in)
  • Niels van Oort - , Technische Universität Delft (Autor:in)
  • Rob M.P. Goverde - , Technische Universität Delft (Autor:in)


Train movement dynamics are usually modelled by means of Newton's second law. The resulting dynamic equation can be very precise if the parameters that it depends on are determined accurately. However, these parameters may vary in time and show wide variations, making the calibration task nontrivial and jeopardizing the performance of a broad variety of applications in the railway industry: from timetable planning and railway traffic simulation to Driver Advisory Systems and Automatic Train Operation. In this article, the online train motion model calibration problem is addressed with a special focus on energy-efficient on-board applications. To this end, location and speed measurements are assumed to be available for a train running under normal operation conditions. A well-known real-time parameter estimation algorithm, the Unscented Kalman Filter, is combined with a driving regime calculator and a post-processing module in order to obtain bounds and statistics of parameters such as the maximum applied tractive effort and power, the applied brake rates, the cruise speed and the length of the final coasting and braking. The proposed framework is tested in a case study with real data from trains operating on the Eindhoven-’s-Hertogenbosch corridor in the Netherlands. Results obtained show that UKF is able to track the speed and location measurements and to estimate the parameters that model the running resistance in the dynamic equation. The proposed driving regime and the post-processing modules can determine the current regime accurately and give a deeper insight into the variations of the driving style, respectively.


FachzeitschriftTransportation Research Part C: Emerging Technologies
PublikationsstatusVeröffentlicht - Okt. 2022
Extern publiziertJa

Externe IDs

ORCID /0000-0003-4111-2255/work/142246308



  • Parameter estimation, Railways, Train motion model calibration, Unscented Kalman Filter