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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F20%3APU138548" target="_blank" >RIV/00216305:26110/20:PU138548 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.hindawi.com/journals/jat/2020/8841810/" target="_blank" >https://www.hindawi.com/journals/jat/2020/8841810/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2020/8841810" target="_blank" >10.1155/2020/8841810</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/CK01000091" target="_blank" >CK01000091: Výhybka 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF ADVANCED TRANSPORTATION

ISSN

0197-6729

e-ISSN

2042-3195

Svazek periodika

2020

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

000598343000004

EID výsledku v databázi Scopus

2-s2.0-85097578500