Testing the Smooth Driving of a Train Using a Neural Network
Emilia Koper , Andrzej Kochan
AbstractThis 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.
|Journal series||Sustainability, ISSN 2071-1050|
|Publication size in sheets||0.65|
|Keywords in English||driving smoothness; train neural emulator; neural networks|
|ASJC Classification||; ;|
|License||Journal (articles only); author's original; ; after publication|
|Score||= 70.0, 20-07-2020, ArticleFromJournal|
|Publication indicators||= 0; : 2016 = 0.911; : 2018 = 2.592 (2) - 2018=2.801 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.