Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists

The objective of this study is to analysis of accident of motorcyclists on Bogotá roads in Colombia. For detection of conditions related to crashes and their severity, the proposed model develops the strategies to enhance road safety. In this context, data mining and machine learning techniques are...

Full description

Autores:
Ospina-Mateus, Holman
Quintana Jiménez, Leonardo Augusto
Lopez-Valdes, Francisco J.
Berrio Garcia, Shyrle
Barrero, Lope H.
Sankar Sana, Shib
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12240
Acceso en línea:
https://hdl.handle.net/20.500.12585/12240
Palabra clave:
Crash Injuries;
Crash;
Random Parameters
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/12240
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
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dc.title.spa.fl_str_mv Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
title Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
spellingShingle Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
Crash Injuries;
Crash;
Random Parameters
LEMB
title_short Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
title_full Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
title_fullStr Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
title_full_unstemmed Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
title_sort Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists
dc.creator.fl_str_mv Ospina-Mateus, Holman
Quintana Jiménez, Leonardo Augusto
Lopez-Valdes, Francisco J.
Berrio Garcia, Shyrle
Barrero, Lope H.
Sankar Sana, Shib
dc.contributor.author.none.fl_str_mv Ospina-Mateus, Holman
Quintana Jiménez, Leonardo Augusto
Lopez-Valdes, Francisco J.
Berrio Garcia, Shyrle
Barrero, Lope H.
Sankar Sana, Shib
dc.subject.keywords.spa.fl_str_mv Crash Injuries;
Crash;
Random Parameters
topic Crash Injuries;
Crash;
Random Parameters
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description The objective of this study is to analysis of accident of motorcyclists on Bogotá roads in Colombia. For detection of conditions related to crashes and their severity, the proposed model develops the strategies to enhance road safety. In this context, data mining and machine learning techniques are used to investigate 34,232 accidents by motorcyclists during January 2013 to February 2018. Both the Genetic algorithm and simulated annealing are applied in conjunction with mining rules (support, confidence, lift, and comprehensibility) as per objectives of the problem. The application of a hybrid algorithm allows for the creation and definition of optimal hierarchical decision rules for the prediction of the severity of motorcycle traffic accidents. The proposed method yields good results in the metrics of recall (90.07%), precision (89.87%), and accuracy (90.06%) on the data set. The results increase the prediction by 20–21% in comparisons with the following methods: Decision Trees (CART, ID3, and C4.5), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Naive Bayes, Neural Networks, Random Forest, and Random Tree. The proposed method defines 11 rules for the prediction of accidents with material damage, 24 rules with injuries, and 12 rules with fatalities. The variables with the most recurrence in the definition of rules are time, weather and road conditions, and the number of victims involved in the accidents. Finally, the interactions of the conditions and characteristics presented in motorcycle accidents are analyzed which contribute to the definition of countermeasures for road safety. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021
dc.date.accessioned.none.fl_str_mv 2023-07-19T21:25:57Z
dc.date.available.none.fl_str_mv 2023-07-19T21:25:57Z
dc.date.submitted.none.fl_str_mv 2023
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dc.identifier.citation.spa.fl_str_mv Ospina-Mateus, H., Quintana Jiménez, L. A., Lopez-Valdes, F. J., Berrio Garcia, S., Barrero, L. H., & Sana, S. S. (2021). Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists. Journal of ambient intelligence and humanized computing, 12(11), 10051-10072.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12240
dc.identifier.doi.none.fl_str_mv 10.1007/s12652-020-02759-5
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Ospina-Mateus, H., Quintana Jiménez, L. A., Lopez-Valdes, F. J., Berrio Garcia, S., Barrero, L. H., & Sana, S. S. (2021). Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists. Journal of ambient intelligence and humanized computing, 12(11), 10051-10072.
10.1007/s12652-020-02759-5
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12240
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Journal of Ambient Intelligence and Humanized Computing
institution Universidad Tecnológica de Bolívar
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spelling Ospina-Mateus, Holman175019f3-96bf-4b48-b2a3-88f94192664bQuintana Jiménez, Leonardo Augustofadbc121-f32c-427a-ae59-4f4a37ed4471Lopez-Valdes, Francisco J.5a4268e2-55b2-412f-814e-1a12bd7d3511Berrio Garcia, Shyrlec1949507-5a55-4c89-bb1c-c88d040346eaBarrero, Lope H.7200fd84-3fac-4d77-9788-f5bca6d52db4Sankar Sana, Shibd10ac6cd-87f8-493f-80ec-cf649edaf60c2023-07-19T21:25:57Z2023-07-19T21:25:57Z20212023Ospina-Mateus, H., Quintana Jiménez, L. A., Lopez-Valdes, F. J., Berrio Garcia, S., Barrero, L. H., & Sana, S. S. (2021). Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists. Journal of ambient intelligence and humanized computing, 12(11), 10051-10072.https://hdl.handle.net/20.500.12585/1224010.1007/s12652-020-02759-5Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe objective of this study is to analysis of accident of motorcyclists on Bogotá roads in Colombia. For detection of conditions related to crashes and their severity, the proposed model develops the strategies to enhance road safety. In this context, data mining and machine learning techniques are used to investigate 34,232 accidents by motorcyclists during January 2013 to February 2018. Both the Genetic algorithm and simulated annealing are applied in conjunction with mining rules (support, confidence, lift, and comprehensibility) as per objectives of the problem. The application of a hybrid algorithm allows for the creation and definition of optimal hierarchical decision rules for the prediction of the severity of motorcycle traffic accidents. The proposed method yields good results in the metrics of recall (90.07%), precision (89.87%), and accuracy (90.06%) on the data set. The results increase the prediction by 20–21% in comparisons with the following methods: Decision Trees (CART, ID3, and C4.5), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), Naive Bayes, Neural Networks, Random Forest, and Random Tree. The proposed method defines 11 rules for the prediction of accidents with material damage, 24 rules with injuries, and 12 rules with fatalities. The variables with the most recurrence in the definition of rules are time, weather and road conditions, and the number of victims involved in the accidents. Finally, the interactions of the conditions and characteristics presented in motorcycle accidents are analyzed which contribute to the definition of countermeasures for road safety. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.application/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Ambient Intelligence and Humanized ComputingExtraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclistsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Crash Injuries;Crash;Random ParametersLEMBCartagena de IndiasAbdelwahab, H.T., Abdel-Aty, M.A. Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections (2001) Transportation Research Record, (1746), pp. 6-13. 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Cited 30 times. http://www.tandf.co.uk/journals/titles/17457300.asp doi: 10.1080/17457300.2014.908224http://purl.org/coar/resource_type/c_6501LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12240/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53ORIGINALScopus - Document details - Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by motorcyclists.pdfScopus - Document details - Extraction of decision rules using genetic algorithms and simulated annealing for prediction of severity of traffic accidents by 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