The Impact of Train Positioning Inaccuracies on Railway Traffic Management Systems: Framework Development and Impacts on TMS Functions

Abstract

Nowadays the railway industry is beginning to give serious consideration to using intelligent traffic management systems (TMSs) in order to improve railway performance regarding train and passenger delays and robust use of capacity. The TMS is responsible for handling railway traffic once a disturbance happens. A fundamental input parameter of a TMS is the train positions, to be used for traffic re‐planning purposes. Inaccuracy in the train positioning data could significantly influence the effectiveness of a TMS. In this study, the authors developed a framework to evaluate how inaccuracies in the train position reporting may affect the TMS performance. This is achieved by assessing the impact of adding inaccuracies to the train position reported to a simulated TMS as it handles operational disturbances in real‐time. The performance of the TMS is analysed by considering variability in overall delay outcomes after re‐planning based on using accurate/inaccurate positional data. They demonstrate the usefulness of their framework in determining the positional accuracy required for the effective application of a basic rescheduling system via an example on a bottleneck area. Results show how the positioning inaccuracies can affect TMS and thus the overall delay.

Full text

IET Intelligent Transport Systems
Research Article
Impact of train positioning inaccuracies on
railway traffic management systems:
framework development and impacts on TMS
functions
ISSN 1751-956X
Received on 31st July 2019
Revised 19th November 2019
Accepted on 24th January 2020
E-First on 4th May 2020
doi: 10.1049/iet-its.2019.0503
www.ietdl.org
Hassan Abdulsalam Hamid1,2 , Gemma L. Nicholson1, Clive Roberts1
1Birmingham Centre for Railway Research and Education, University of Birmingham, Birmingham, UK
2Hawija Technical Institute, Northern Technical University, Kirkuk, Iraq
E-mail: HAH496@alumni.bham.ac.uk
Abstract: Nowadays the railway industry is beginning to give serious consideration to using intelligent traffic management
systems (TMSs) in order to improve railway performance regarding train and passenger delays and robust use of capacity. The
TMS is responsible for handling railway traffic once a disturbance happens. A fundamental input parameter of a TMS is the train
positions, to be used for traffic re-planning purposes. Inaccuracy in the train positioning data could significantly influence the
effectiveness of a TMS. In this study, the authors developed a framework to evaluate how inaccuracies in the train position
reporting may affect the TMS performance. This is achieved by assessing the impact of adding inaccuracies to the train position
reported to a simulated TMS as it handles operational disturbances in real-time. The performance of the TMS is analysed by
considering variability in overall delay outcomes after re-planning based on using accurate/inaccurate positional data. They
demonstrate the usefulness of their framework in determining the positional accuracy required for the effective application of a
basic rescheduling system via an example on a bottleneck area. Results show how the positioning inaccuracies can affect TMS
and thus the overall delay.
1 Introduction
In recent years, the demand for rail transport from both passengers
and freight carriers has increased. Rail infrastructure managers and
operators have been put under pressure to make more of existing
resources. This may motivate them to go in the direction of using
intelligent traffic management systems (TMSs) in order to make
more effective use of available capacity. TMS needs a variety of
input data and the real-time train position is a vital factor. A train's
position is generally specified, in a railway, as the linear distance
that it has travelled along the tracks of a specific route from a
reference point [1]. Subsequently, the train speed and direction can
be estimated by inference, from the position and time between
successive recording points [2, 3]. The train positioning method
that is typically used by railway systems today relies on track
signalling technology in a fixed block system. Such a signalling
system divides the track into sections known as blocks and
indicates whether specific block sections are occupied by a train or
not. Therefore, train position information is available for all trains
in near real-time at the traffic control centre (TCC) [2]. The
inaccuracies of train positioning data using fixed block signalling
technology are related to the section length [4]. The track
occupation information is supplied to TCC by trackside equipment
[5, 6].
The train positioning system of the European Railway TMS
(ERTMS) is based on an odometry system using balises to fine-
tune the odometer measurements. The accuracy of the system is
based on the odometry accuracy and the distance between
consecutive balises. The train positioning inaccuracies (TPI) of an
ERTMS system must be arranged to be less than ±(5 m + 5%) of
the distance travelled since the last balise [7, 8]. Many studies have
proposed alternative or supplementary systems, such as global
navigation satellite systems (GNSSs) [9], inertial navigation
systems (INSs) [10], inertial measurement units [11] etc. to
improve the train positioning accuracy in real-time. There is a rich
body of the literature and overview of models and methods used
for dealing with precise train positioning; for example, different
train positioning solutions are reviewed in [12, 13]. However, there
are limited references addressing the required train positioning
accuracy for some railway applications.
The train control system needs to be combined with real-time
advanced decision support systems or automated TMS in order to
improve dispatching decisions and to intelligently manage the
routes and orders of trains. Some proposed TMS also aim to
automatically manage the speed of the trains [14]. TMS is
generally focussed on monitoring the trains’ movement on the
railway network taking into account the planned timetable, and
recovering a disrupted train or trains by rescheduling and returning
trains back to the original timetable as soon as possible.
To the best of our knowledge, the full impact of TPI on the
effectiveness of a TMS is still unclear. The TMS uses train position
data to monitor, predict and solve potential traffic conflicts. The
accuracy of train position information is varied, based on the
positioning technology that is used. The inaccuracies of train
positioning data may have a significant effect on TMS performance
and lead the TMS to provide a poorly optimised solution.
Therefore in this study, a framework to evaluate how inaccuracies
in the train position reporting may affect the TMS performance is
proposed. The framework is implemented to attain a preliminary
understanding of the positioning inaccuracy effect on the
fundamental TMS functions. The study indicates where the
positioning inaccuracy has a great impact and how this inaccuracy
can influence the TMS functions. The framework is used to
demonstrate the impact of the worst-case scenario of positioning on
the TMS outcome. So, after that, the desired position accuracy
associated with the acceptable TMS performance can be
characterised.
2 Literature review
Many studies have developed models and methods for building an
intelligent TMS that can provide improved dispatching decisions
taking based on different goals, e.g. saving energy, increasing
capacity use and reducing delays [15–17]. There are also studies
investigating the impact of some parameters on TMS performance;
for instance, the impact of using a flexible timetable on TMS
IET Intell. Transp. Syst., 2020, Vol. 14 Iss. 6, pp. 534-544
© The Institution of Engineering and Technology 2020
534