Geosimulation as a tool for the prevention of traffic accidents
Traffic accidents represent a never-ending tragedy, and according to the World Health Organization (2018), 1.33 million people die in the world every year [1]. Most efforts in modeling phenomena of a dynamic nature have focused on working with static snapshots that reduce the natural depth of the wo...
- Autores:
-
amelec, viloria
Varela Izquierdo, Noel
Ortiz-Ospino, Luis Eduardo
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7705
- Acceso en línea:
- https://hdl.handle.net/11323/7705
https://doi.org/10.1007/978-981-15-7234-0_83
https://repositorio.cuc.edu.co/
- Palabra clave:
- Traffic accidents
Geosimulation
Agent-based modeling
Geographic information systems
Dynamic models
Traffix
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| id |
RCUC2_7298ee2c8515498e21e7fdd0055dc711 |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/7705 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Geosimulation as a tool for the prevention of traffic accidents |
| title |
Geosimulation as a tool for the prevention of traffic accidents |
| spellingShingle |
Geosimulation as a tool for the prevention of traffic accidents Traffic accidents Geosimulation Agent-based modeling Geographic information systems Dynamic models Traffix |
| title_short |
Geosimulation as a tool for the prevention of traffic accidents |
| title_full |
Geosimulation as a tool for the prevention of traffic accidents |
| title_fullStr |
Geosimulation as a tool for the prevention of traffic accidents |
| title_full_unstemmed |
Geosimulation as a tool for the prevention of traffic accidents |
| title_sort |
Geosimulation as a tool for the prevention of traffic accidents |
| dc.creator.fl_str_mv |
amelec, viloria Varela Izquierdo, Noel Ortiz-Ospino, Luis Eduardo Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Varela Izquierdo, Noel Ortiz-Ospino, Luis Eduardo Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Traffic accidents Geosimulation Agent-based modeling Geographic information systems Dynamic models Traffix |
| topic |
Traffic accidents Geosimulation Agent-based modeling Geographic information systems Dynamic models Traffix |
| description |
Traffic accidents represent a never-ending tragedy, and according to the World Health Organization (2018), 1.33 million people die in the world every year [1]. Most efforts in modeling phenomena of a dynamic nature have focused on working with static snapshots that reduce the natural depth of the world’s dynamics to simplify it, abstracting perspectives that are fixed or static in some way. In the case of traffic accidents, most models used are those based on the principle of cause and effect, where the appearance of one or several variables gives rise to the event, like a domino effect. In this research, the problem of traffic accident avoidance was addressed through the use of a dynamic type model, based on the technique called geosimulation, where all the elements involved are interrelated. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-01-18T14:16:09Z |
| dc.date.available.none.fl_str_mv |
2021-01-18T14:16:09Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/7705 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1007/978-981-15-7234-0_83 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| url |
https://hdl.handle.net/11323/7705 https://doi.org/10.1007/978-981-15-7234-0_83 https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
1. Marston S, Li Z, Bandyopadhyay S, Zhang J, Ghalsas A (2011) Cloud computing—the business perspective. Dec Supp Syst 51(1):176–189 2. Bifet A, De Francisci Morales G (2014) Big data stream learning with Samoa. 3. Lomax T, Schrank D, Turner S, Margiotta R (2003) Report for selecting travel reliability measures. Federal Highway Administration, Washington, DC 4. Anderson JA (2007) In: S.A. de C.V. (ed) Redes neuronales, 1a ed. Alfa Omega Gru-po Editor, México, pp 120–125. ISBN 9789701512654 5. Pardillo J, Sánchez V (2015) Apuntes de Ingeniería de Tránsito. ETS Ingenieros de Caminos, Canales y Puertos, Madrid, España 6. Skabardonis A, Varaiya P, Petty K (2003) Measuring recurrent and non-recurrent traffic congestion. Transp Res Rec J Transp Res Board 1856:60–68 7. U.S. Department of Transportation (2004) Archived data management systems—a cross-cutting study. Publication FHWA-JPO-05–044. FHWA, U.S. Department of Transportation 8. Yong-chuan Z, Xiao-qing Z, li-ting Z, Zhen-ting C (2011) Traffic congestion detection based on GPS floating-car data. Proc Eng 15:5541–5546 9. Thames L, Schaefer D (2016) Software defined cloud manufacturing for industry 4.0. Procedía CIRP 52:12–17 10. Viloria A, Neira-Rodado D, Lezama OBP (2019) Recovery of scientific data using intelligent distributed data warehouse. In: ANT/EDI40 2019, pp 1249–1254 11. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. In: ANT/EDI40 2019, pp 1201–1206 12. Alcalá R, Alcalá-Fdez J, Herrera F (2007) A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–635 13. Alpaydin E (2004) Introduction to machine learning. The MIT Press, Massachusetts 14. Álvarez P, Hadi M, Zhan C (2010) Using Intelligent transportation systems data archives for traffic simulation applications. Transp Res Rec J Transp Res Board 2161:29–39 15. Bizama J (2012) Modelación y simulación mediante un microsimulador de la zona de influencia del Puente Llacolén. Memoria de Título, Universidad del Bio Bio 16. Levinson H, Rakha H (2010) Analytical procedures for determining the impacts of reliability mitigation strategies. Cambridge Systematics, Texas A&M University, Dowling Associates, Street Smarts 17. Cortés CE, Gibson J, Gschwender A, Munizaga M, Zúñiga M (2011) Commercial bus speed diagnosis based on GPS-monitored data. Transp Res Part C 19(4):695–707 18. Diker AC (2012) Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPA data. In: 2012 IV international conference problems of cybernetics and informatics (PCI), Baku, Azerbaijan 19. Amelec V (2015) Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv Sci Lett 21(5):1406–1408 20. Viloria A, Robayo PV (2016) Inventory reduction in the supply chain of finished products for multinational companies. Indian J Sci Technol 8(1) |
| dc.rights.spa.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.source.spa.fl_str_mv |
Advances in Intelligent Systems and Computing |
| institution |
Corporación Universidad de la Costa |
| dc.source.url.spa.fl_str_mv |
https://link.springer.com/chapter/10.1007/978-981-15-7234-0_83 |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/f0909a82-12f0-4bb0-8189-86f89ece1de6/download https://repositorio.cuc.edu.co/bitstreams/cce75ceb-a0e9-4e6c-b401-c9570cb3d60d/download https://repositorio.cuc.edu.co/bitstreams/2c7ccb7d-1e1c-4ebf-b0d0-678d660e8d91/download https://repositorio.cuc.edu.co/bitstreams/217c6b99-9e3b-44d7-a135-a8d695946645/download https://repositorio.cuc.edu.co/bitstreams/ce078302-4308-4368-9105-fd587da9b15d/download https://repositorio.cuc.edu.co/bitstreams/90d8b5c8-59bd-4580-91fb-ff63a9e1ac46/download |
| bitstream.checksum.fl_str_mv |
1d4e4978a6f9b948279fb42366ad5480 4460e5956bc1d1639be9ae6146a50347 e30e9215131d99561d40d6b0abbe9bad 30d5cdbd22a3a5d820ecfa2eecd518c2 30d5cdbd22a3a5d820ecfa2eecd518c2 90bb0b13f9a091c6211e3550e8ece36b |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760783905783808 |
| spelling |
amelec, viloriaVarela Izquierdo, NoelOrtiz-Ospino, Luis EduardoPineda Lezama, Omar Bonerge2021-01-18T14:16:09Z2021-01-18T14:16:09Z2021https://hdl.handle.net/11323/7705https://doi.org/10.1007/978-981-15-7234-0_83Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Traffic accidents represent a never-ending tragedy, and according to the World Health Organization (2018), 1.33 million people die in the world every year [1]. Most efforts in modeling phenomena of a dynamic nature have focused on working with static snapshots that reduce the natural depth of the world’s dynamics to simplify it, abstracting perspectives that are fixed or static in some way. In the case of traffic accidents, most models used are those based on the principle of cause and effect, where the appearance of one or several variables gives rise to the event, like a domino effect. In this research, the problem of traffic accident avoidance was addressed through the use of a dynamic type model, based on the technique called geosimulation, where all the elements involved are interrelated.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Ortiz-Ospino, Luis Eduardo-will be generated-orcid-0000-0002-9334-4026-600Pineda Lezama, Omar Bonergeapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_83Traffic accidentsGeosimulationAgent-based modelingGeographic information systemsDynamic modelsTraffixGeosimulation as a tool for the prevention of traffic accidentsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Marston S, Li Z, Bandyopadhyay S, Zhang J, Ghalsas A (2011) Cloud computing—the business perspective. Dec Supp Syst 51(1):176–1892. Bifet A, De Francisci Morales G (2014) Big data stream learning with Samoa.3. Lomax T, Schrank D, Turner S, Margiotta R (2003) Report for selecting travel reliability measures. Federal Highway Administration, Washington, DC4. Anderson JA (2007) In: S.A. de C.V. (ed) Redes neuronales, 1a ed. Alfa Omega Gru-po Editor, México, pp 120–125. ISBN 97897015126545. Pardillo J, Sánchez V (2015) Apuntes de Ingeniería de Tránsito. ETS Ingenieros de Caminos, Canales y Puertos, Madrid, España6. Skabardonis A, Varaiya P, Petty K (2003) Measuring recurrent and non-recurrent traffic congestion. Transp Res Rec J Transp Res Board 1856:60–687. U.S. Department of Transportation (2004) Archived data management systems—a cross-cutting study. Publication FHWA-JPO-05–044. FHWA, U.S. Department of Transportation8. Yong-chuan Z, Xiao-qing Z, li-ting Z, Zhen-ting C (2011) Traffic congestion detection based on GPS floating-car data. Proc Eng 15:5541–55469. Thames L, Schaefer D (2016) Software defined cloud manufacturing for industry 4.0. Procedía CIRP 52:12–1710. Viloria A, Neira-Rodado D, Lezama OBP (2019) Recovery of scientific data using intelligent distributed data warehouse. In: ANT/EDI40 2019, pp 1249–125411. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. In: ANT/EDI40 2019, pp 1201–120612. Alcalá R, Alcalá-Fdez J, Herrera F (2007) A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–63513. Alpaydin E (2004) Introduction to machine learning. The MIT Press, Massachusetts14. Álvarez P, Hadi M, Zhan C (2010) Using Intelligent transportation systems data archives for traffic simulation applications. Transp Res Rec J Transp Res Board 2161:29–3915. Bizama J (2012) Modelación y simulación mediante un microsimulador de la zona de influencia del Puente Llacolén. Memoria de Título, Universidad del Bio Bio16. Levinson H, Rakha H (2010) Analytical procedures for determining the impacts of reliability mitigation strategies. Cambridge Systematics, Texas A&M University, Dowling Associates, Street Smarts17. Cortés CE, Gibson J, Gschwender A, Munizaga M, Zúñiga M (2011) Commercial bus speed diagnosis based on GPS-monitored data. Transp Res Part C 19(4):695–70718. Diker AC (2012) Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPA data. In: 2012 IV international conference problems of cybernetics and informatics (PCI), Baku, Azerbaijan19. Amelec V (2015) Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv Sci Lett 21(5):1406–140820. Viloria A, Robayo PV (2016) Inventory reduction in the supply chain of finished products for multinational companies. Indian J Sci Technol 8(1)PublicationORIGINALGeosimulation as a Tool for the Prevention of Traffic Accidents.pdfGeosimulation as a Tool for the Prevention of Traffic Accidents.pdfapplication/pdf98209https://repositorio.cuc.edu.co/bitstreams/f0909a82-12f0-4bb0-8189-86f89ece1de6/download1d4e4978a6f9b948279fb42366ad5480MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/cce75ceb-a0e9-4e6c-b401-c9570cb3d60d/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/2c7ccb7d-1e1c-4ebf-b0d0-678d660e8d91/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILGeosimulation as a Tool for the Prevention of Traffic Accidents.pdf.jpgGeosimulation as a Tool for the Prevention of Traffic Accidents.pdf.jpgimage/jpeg30280https://repositorio.cuc.edu.co/bitstreams/217c6b99-9e3b-44d7-a135-a8d695946645/download30d5cdbd22a3a5d820ecfa2eecd518c2MD54THUMBNAILGeosimulation as a Tool for the Prevention of Traffic Accidents.pdf.jpgGeosimulation as a Tool for the Prevention of Traffic Accidents.pdf.jpgimage/jpeg30280https://repositorio.cuc.edu.co/bitstreams/ce078302-4308-4368-9105-fd587da9b15d/download30d5cdbd22a3a5d820ecfa2eecd518c2MD54TEXTGeosimulation as a Tool for the Prevention of Traffic Accidents.pdf.txtGeosimulation as a Tool for the Prevention of Traffic Accidents.pdf.txttext/plain1115https://repositorio.cuc.edu.co/bitstreams/90d8b5c8-59bd-4580-91fb-ff63a9e1ac46/download90bb0b13f9a091c6211e3550e8ece36bMD5511323/7705oai:repositorio.cuc.edu.co:11323/77052024-09-17 11:08:22.307http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |