Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-16020
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Main Title: Machine learning tools in the analyze of a bike sharing system
Author(s): Babic, Matej
Fragassa, Cristiano
Marinkovic, Dragan
Povh, Janez
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/17241
http://dx.doi.org/10.14279/depositonce-16020
License: https://creativecommons.org/licenses/by-nc/4.0/
Abstract: Advanced models, based on artificial intelligence and machine learning, are used here to analyze a bike-sharing system. The specific target was to predict the number of rented bikes in the Nova Mesto (Slovenia) public bike share scheme. For this purpose, the topological properties of the transport network were determined and related to the weather conditions. Pajek software was used and the system behavior during a 30-week period was investigated. Open questions were, for instance: how many bikes are shared in different weather conditions? How the network topology impacts the bike sharing system? By providing a reasonable answer to these and similar questions, several accurate ways of modeling the bike sharing system which account for both topological properties and weather conditions, were developed and used for its optimization.
Subject(s): transportation systems engineering
bike-sharing system
artificial intelligence
machine learning
hybrid intelligent systems
weather conditions
Issue Date: 2022
Date Available: 18-Jul-2022
Language Code: en
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Journal Title: International Journal for Quality Research
Publisher: Center for Quality, Univ. of Montenegro
Volume: 16
Issue: 2
Publisher DOI: 10.24874/IJQR16.02-04
Page Start: 375
Page End: 394
EISSN: 1800-7473
ISSN: 1800-6450
TU Affiliation(s): Fak. 5 Verkehrs- und Maschinensysteme » Inst. Mechanik » FG Strukturmechanik und Strukturberechnung
Appears in Collections:Technische Universität Berlin » Publications

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