Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data

Loading...
Thumbnail Image
Date
2020-11-24
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi
Altmetrics
Abstract
This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.
Description
Citation
JOURNAL OF ADVANCED TRANSPORTATION. 2020, vol. 2020, issue 1, p. 1-10.
https://www.hindawi.com/journals/jat/2020/8841810/
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
Creative Commons Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
Citace PRO