Testing the Smooth Driving of a Train Using a Neural Network

Emilia Koper , Andrzej Kochan


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.
Author Emilia Koper (FT / TCTI)
Emilia Koper,,
- Division of Traffic Control and Transport Infrastructure
, Andrzej Kochan (FT / TCTI)
Andrzej Kochan,,
- Division of Traffic Control and Transport Infrastructure
Journal seriesSustainability, ISSN 2071-1050
Issue year2020
Publication size in sheets0.65
Article number4622
Keywords in English driving smoothness; train neural emulator; neural networks
ASJC Classification3305 Geography, Planning and Development; 2105 Renewable Energy, Sustainability and the Environment; 2308 Management, Monitoring, Policy and Law
Languageen angielski
LicenseJournal (articles only); author's original; Uznanie Autorstwa (CC-BY); after publication
WUT535553992c7f4f82a3dd485e68b269ee.pdf 1.5 MB
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 20-07-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.911; WoS Impact Factor: 2018 = 2.592 (2) - 2018=2.801 (5)
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