Development and Comparison of Algorithms to Derive Vehicle Location from Speed Profile Data

Abstract

The rapid increases in the quantity of data being gathered regarding technological systems such as railways can promote improvements in their design and operation. Combining information from different datasets allows more in-depth analysis, such as using train location data to enhance the analysis of speed profiles and energy consumption. Positioning systems such as GPS are frequently used to obtain this information, but are not necessarily always available, such as in underground metro systems. The focus of this paper is therefore the development of algorithms to derive train location information from measured speed profile data and network topology. Two different algorithms were developed to extract individual station-to-station journeys from an example consisting of a dataset of speed profiles and energy consumption from an urban rail system, and four classification algorithms were developed to identify the station pairs associated with each journey. It was found that the best-performing approach for this task was to compare the cumulative distance of a group of several consecutive journeys against a database of station-to-station distances to find the best match. This was more resilient than constructing sequences of consecutive journeys from possible matches in a database of station-to-station distances and orders of magnitude faster than heuristic algorithms.

Keywords

Speed profile / Positioning / Location / Algorithms / Large datasets

References

[1] Longo G, Medeossi G, Nash A (2012) Estimating train motion using detailed sensor data. In: Transportation research board 91st annual meeting, Washington D.C., USA, 22nd–26th January 2012

[2] Pritchard J, Preston J, Armstrong J. Making meaningful comparisons between road and rail—substituting average energy consumption data for rail with empirical analysis. Transp Plan Technol, 2015, 38(1): 111-130

[3] Geistler A. Allan J, Hill RJ, Brebbia CA, Scuitto G, Sone S. Train location with eddy current sensors. Computers in railways VIII, 2002, Ashurst: WIT Press 1053-1062

[4] Heirich O, Lehner A, Robertson P, Strang T (2011) Measurement and analysis of train motion and railway track characteristics with inertial sensors. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC), 5–7 Oct 2011, pp 1995–2000. https://doi.org/10.1109/itsc.2011.6082908

[5] Powell JP, Palacín R. Passenger stability within moving railway vehicles: limits on maximum longitudinal acceleration. Urban Rail Transit, 2015, 1(2): 95-103

[6] Allotta B, Colla V, Malvezzi M. Train position and speed estimation using wheel velocity measurements. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2002, 216(3): 207-225

[7] Malvezzi M, Vettori G, Allotta B, Pugi L, Ridolfi A, Rindi A. A localization algorithm for railway vehicles based on sensor fusion between tachometers and inertial measurement units. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2014, 228(4): 431-448

[8] Ernest P, Mazl R, Preucil L (2004) Train locator using inertial sensors and odometer. In: IEEE intelligent vehicles symposium, 2004, 14–17 June 2004, pp 860–865. https://doi.org/10.1109/ivs.2004.1336497

[9] Druet E, Bilenne O, Massar M, Meers F, Lefèvre F (2006) Interval-based state estimation for safe train positioning. In: 7th world congress on railway research, Montreal, Canada, 4th–8th June 2006

[10] De Fabris S, Longo G, Medeossi G. Hansen IA. Automated analysis of train event recorder data to improve micro-simulation models. Timetable planning and information quality, 2010, Ashurst: WIT Press 125-134.

[11] Powell JP, Palacin R. Stile di guida con ERTMS livello 2 e segnalamento convenzionale lungo linea: uno studio esplorativo [Driving style for ERTMS in-cab and conventional line side signalling: an exploratory study]. Ingegneria Ferroviaria, 2016, 71(12): 927-942.