Testing the Smooth Driving of a Train Using a Neural Network

Emilia Koper and Andrzej Kochan
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Emilia Koper: Faculty of Transport, Warsaw University of Technology, 00662 Warsaw, Poland
Andrzej Kochan: Faculty of Transport, Warsaw University of Technology, 00662 Warsaw, Poland

Sustainability, 2020, vol. 12, issue 11, 1-14

Abstract: 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.

Keywords: driving smoothness; train neural emulator; neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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