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Implementation of a Self-Enforcing Network to Identify Determinants of the WiFi Quality on German Highspeed Trains

Authors:
Jonathan Berrisch
Timo Rammert
Christina Klüver

Keywords: self-organized learning; cue validity factor; Intelligent data analysis; Industry 4.0 data analysis.

Abstract:
In this paper, we demonstrate how to analyze the WiFi data of the German highspeed trains called InterCityExpress (ICE) on the basis of a neural network. To achieve this, we apply a Self-Enforcing Network with cue validity factors to underline the importance of selected features. It is shown that the quality of the WiFi connection, in terms of the rate of downloads and the latency, can be grouped and explained by just a few determinants. We will show where the network coverage is especially good or bad and suggest ways to improve this quality to enhance the comfort of traveling on the highspeed trains and therefore to possible expand the profits of the operating company.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2019

Publication date: June 30, 2019

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-61208-723-8

Location: Rome, Italy

Dates: from June 30, 2019 to July 4, 2019