Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]

Descripción del Articulo

Este trabajo ha sido parcialmente financiado por ”Cienciactiva – CONCYTEC” del gobierno peruano, bajo el número de proyecto 128-2015-FONDECYT y por el ”Programa Nacional de Innovación para la Competiti-vidad y Productividad, Innóvate - Perúc¸on número de contrato FINCyT 363-PNICP-PIAP-2014....

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Detalles Bibliográficos
Autores: Lovón-Melgarejo J., Tenorio-Trigoso A., Castillo-Cara M., Miranda D.
Formato: objeto de conferencia
Fecha de Publicación:2018
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:español
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/933
Enlace del recurso:https://hdl.handle.net/20.500.12390/933
https://doi.org/10.18687/LACCEI2018.1.1.413
Nivel de acceso:acceso abierto
Materia:Smart City
Machine learning
Open Data
https://purl.org/pe-repo/ocde/ford#5.07.04
id CONC_14f31086065aa2114864bc54ec8d953b
oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/933
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
title Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
spellingShingle Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
Lovón-Melgarejo J.
Smart City
Machine learning
Open Data
https://purl.org/pe-repo/ocde/ford#5.07.04
title_short Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
title_full Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
title_fullStr Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
title_full_unstemmed Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
title_sort Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
author Lovón-Melgarejo J.
author_facet Lovón-Melgarejo J.
Tenorio-Trigoso A.
Castillo-Cara M.
Miranda D.
author_role author
author2 Tenorio-Trigoso A.
Castillo-Cara M.
Miranda D.
author2_role author
author
author
dc.contributor.author.fl_str_mv Lovón-Melgarejo J.
Tenorio-Trigoso A.
Castillo-Cara M.
Miranda D.
dc.subject.none.fl_str_mv Smart City
topic Smart City
Machine learning
Open Data
https://purl.org/pe-repo/ocde/ford#5.07.04
dc.subject.es_PE.fl_str_mv Machine learning
Open Data
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.07.04
description Este trabajo ha sido parcialmente financiado por ”Cienciactiva – CONCYTEC” del gobierno peruano, bajo el número de proyecto 128-2015-FONDECYT y por el ”Programa Nacional de Innovación para la Competiti-vidad y Productividad, Innóvate - Perúc¸on número de contrato FINCyT 363-PNICP-PIAP-2014.
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/933
dc.identifier.doi.none.fl_str_mv https://doi.org/10.18687/LACCEI2018.1.1.413
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85057447347
url https://hdl.handle.net/20.500.12390/933
https://doi.org/10.18687/LACCEI2018.1.1.413
identifier_str_mv 2-s2.0-85057447347
dc.language.iso.none.fl_str_mv spa
language spa
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.uri.none.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.publisher.none.fl_str_mv Latin American and Caribbean Consortium of Engineering Institutions
publisher.none.fl_str_mv Latin American and Caribbean Consortium of Engineering Institutions
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
_version_ 1844883126887645184
spelling Publicationrp02469600rp02468600rp01248500rp02214500Lovón-Melgarejo J.Tenorio-Trigoso A.Castillo-Cara M.Miranda D.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2018https://hdl.handle.net/20.500.12390/933https://doi.org/10.18687/LACCEI2018.1.1.4132-s2.0-85057447347Este trabajo ha sido parcialmente financiado por ”Cienciactiva – CONCYTEC” del gobierno peruano, bajo el número de proyecto 128-2015-FONDECYT y por el ”Programa Nacional de Innovación para la Competiti-vidad y Productividad, Innóvate - Perúc¸on número de contrato FINCyT 363-PNICP-PIAP-2014.The following work applies Machine Learning algorithms as a tool for a possible solution to the problem of citizen security in a South American city. This application aims to reduce the threat risk to the physical integrity of pedestrians through the geolocation, in real time, using safer places to walk. A database of free disposal for the user is the Open Data San Isidro, district of Lima, Peru, which has been used in the development of this work. This database keeps records of different accidents types (most of the automobile type) occurring in different places of this district, this data will be used to determine safe areas in the route from one place to another, decreasing the probability of suffering an accident. For this work, techniques of non-supervised learning algorithms of Clustering type: k-Means have been used. Likewise, a reduction of dimensions has previously been made using the Principal Component Analysis (PCA) technique.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecspaLatin American and Caribbean Consortium of Engineering Institutionsinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/Smart CityMachine learning-1Open Data-1https://purl.org/pe-repo/ocde/ford#5.07.04-1Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]info:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/933oai:repositorio.concytec.gob.pe:20.500.12390/9332024-05-30 15:59:56.219https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="78ef9c12-40c5-4422-9934-3988d44f60f8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>spa</Language> <Title>Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.18687/LACCEI2018.1.1.413</DOI> <SCP-Number>2-s2.0-85057447347</SCP-Number> <Authors> <Author> <DisplayName>Lovón-Melgarejo J.</DisplayName> <Person id="rp02469" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Tenorio-Trigoso A.</DisplayName> <Person id="rp02468" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Castillo-Cara M.</DisplayName> <Person id="rp01248" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Miranda D.</DisplayName> <Person id="rp02214" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Latin American and Caribbean Consortium of Engineering Institutions</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/licenses/by-nc-nd/4.0/</License> <Keyword>Smart City</Keyword> <Keyword>Machine learning</Keyword> <Keyword>Open Data</Keyword> <Abstract>The following work applies Machine Learning algorithms as a tool for a possible solution to the problem of citizen security in a South American city. This application aims to reduce the threat risk to the physical integrity of pedestrians through the geolocation, in real time, using safer places to walk. A database of free disposal for the user is the Open Data San Isidro, district of Lima, Peru, which has been used in the development of this work. This database keeps records of different accidents types (most of the automobile type) occurring in different places of this district, this data will be used to determine safe areas in the route from one place to another, decreasing the probability of suffering an accident. For this work, techniques of non-supervised learning algorithms of Clustering type: k-Means have been used. Likewise, a reduction of dimensions has previously been made using the Principal Component Analysis (PCA) technique.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.413352
Identification of risk zones for road safety through unsupervised learning algorithms [Identificación de zonas de riesgo para la Seguridad Vial mediante algoritmos de aprendizaje no supervisado]
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