Mathematical Model of the Movement Authority in the ERTMS/ETCS System

Kochan, A.; Koper, Emilia

doi:10.1007/978-3-030-27687-4_22

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…The testing of the indicator requires data sets describing the dynamic profile of train driving under different conditions, as shaped by [27][28][29]: In the case of a run consisting of two phases: starting (continuous line) and driving at momentum to stop (dotted line), the energy consumption will be minimal (jmin), while passenger comfort will the highest, but the run time will be the longest (tmax). In the case of a forced drive, i.e., driving with a direct transition from start to stop (dashed line), the travel time will be the shortest (tmin), but the energy consumption will be maximal (jmax), and the passenger comfort will be the lowest.…”

Section: Neural Train Emulatormentioning

confidence: 99%

“…The testing of the indicator requires data sets describing the dynamic profile of train driving under different conditions, as shaped by [27][28][29] The assessment of the indicator for the different profiles described above will require the actual mapping of the train's behavior over a specific section of the rail network for different driving techniques. In other words, to carry out the tests, it is necessary to acquire the actual driving speed profiles obtained using the train modeling simulator, the value of which are train speed (V), a parameter input value of the traction (drive) adjuster and brake adjuster, and track infrastructure parameters.…”

Section: Neural Train Emulatormentioning

confidence: 99%

“…The preparation of the neural network structure requires an analysis of the simulation conditions and the preparation of an appropriate train model and its environment. At this stage, the results of the authors' research dedicated to the analysis of the movement authority model [27] and simulation of braking curves in the ERTMS/ETCS system [28] will be used. In this approach, the train is described by static and dynamic parameters.…”

Section: Model Of the Trainmentioning

confidence: 99%

“…The model of the trackside infrastructure on which the train runs well reflects the detailed model of the authorisation to run, as detailed in [27]. Movement authority zj is called tuple:…”

Section: Infrastructure Modelmentioning

confidence: 99%

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Testing the Smooth Driving of a Train Using a Neural Network

Koper

Kochan

2020

Sustainability

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This article deals with the extraction of a new original parameter to characterize a railway traffic driving smoothness indicator, and its investigation is based on data obtained from a neural train emulator. This indicator of driving smoothness is an example of the sustainable value of control command and signaling technology. The pro-social and pro-environmental aspects of smooth driving are indicated and the article proposes the introduction of a new indicator for assessing the quality of rail traffic, taking into account traffic on a micro scale—the driving smoothness of a single train (also called driving flow), derived from a parameter identified in the literature—and traffic smoothness (also called traffic flow), describing traffic quality on a macro scale. At the same time, the concept of a neural train emulator is presented, providing input data to determine the value of the proposed indicator for different train models and track systems in order to test the indicator’s properties. The concept proposes the structure of an artificial neural network, the technique of obtaining test data sets and the conditions of training the network as well. An emulator based on the neural network enables the simulation of train driving, taking into account its nonlinearity and data acquisition for indicator research.

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Section: Neural Train Emulatormentioning

confidence: 99%

Section: Neural Train Emulatormentioning

confidence: 99%

Section: Model Of the Trainmentioning

confidence: 99%

“…The model of the trackside infrastructure on which the train runs well reflects the detailed model of the authorisation to run, as detailed in [27]. Movement authority zj is called tuple:…”

Section: Infrastructure Modelmentioning

confidence: 99%

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Testing the Smooth Driving of a Train Using a Neural Network

Koper

Kochan

2020

Sustainability

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“…Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. In [8], the authors presented a proposal for a mathematical model of Movement Authority in the ERTMS/ETCS system, which is an essential characteristic of the route for a train, and paper [9] presents proposals for the development of the ERTMS/ETCS system.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency and Smooth Running of a Train on the Route While Approaching Another Train

Szkopiński¹,

Kochan

2021

Energies

Self Cite

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This paper presents an innovative technique of traffic management on a route divided into fixed block distances for trains equipped with a continuous area data transmission system that ensures information exchange between a train and a control center. The originality of the technique is focused on the optimization of the following train’s speed profile in terms of energy consumption under the need to maintain a minimum distance to the preceding train and to assume the smooth running of the following train, using elements of fixed and mobile block distance methods. The obtained results are an answer to the question of whether it is possible to obtain a smoother movement of the train and savings in mechanical energy consumption if the speed profile of this train is adjusted to the conditions before the train, such as information about the speed of the preceding train.

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ERTMS/ETCS Level 3: Development, assumptions, and what it means for the future

Knutsen

Olsson

2023

J. Int. Con. Veh.

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Mathematical Model of the Movement Authority in the ERTMS/ETCS System

Cited by 4 publications

References 3 publications

Testing the Smooth Driving of a Train Using a Neural Network

Testing the Smooth Driving of a Train Using a Neural Network

Energy Efficiency and Smooth Running of a Train on the Route While Approaching Another Train

ERTMS/ETCS Level 3: Development, assumptions, and what it means for the future

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