Traffic crash injuries occurrence varieties across Barcelona districts

Barcelona (Spain) had a total population of 1,636,762 in 2019 that are distributed on 10 districts across the city. These districts have different characteristics in size and population density. As a result, this can lead to different traffic crashes injuries occurrences and numbers in these areas....

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Detalles Bibliográficos
Autores: Aiash, Ahmad|||0000-0003-1941-4011, Robusté Antón, Francesc|||0000-0001-9433-5386
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/371361
Acceso en línea:https://hdl.handle.net/2117/371361
Access Level:acceso abierto
Palabra clave:Traffic accidents
Barcelona
Districts
Injuries
Traffic crashes
Bayesian network
Accidents de trànsit
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport urbà
id ES_3e39a4c8fc041f8857f726f2c0ecffae
oai_identifier_str oai:upcommons.upc.edu:2117/371361
network_acronym_str ES
network_name_str España
repository_id_str
spelling Traffic crash injuries occurrence varieties across Barcelona districtsAiash, Ahmad|||0000-0003-1941-4011Robusté Antón, Francesc|||0000-0001-9433-5386Traffic accidentsBarcelonaDistrictsInjuriesTraffic crashesBayesian networkAccidents de trànsitÀrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport urbàBarcelona (Spain) had a total population of 1,636,762 in 2019 that are distributed on 10 districts across the city. These districts have different characteristics in size and population density. As a result, this can lead to different traffic crashes injuries occurrences and numbers in these areas. Therefore, this study is attempting to determine the conditional probabilities for each district in order to identify the district that has highest number of injuries compared to other areas. A Bayesian network approach is utilized to analyze the dataset and identify the high-risk district alongside analyzing the varieties of traffic crashes during four-year intervals. The results have shown that the district that has the highest population, highest usage of private transport mode, and highest density of passenger cars per km2 (compared to all other districts), has the highest risk of having all types of injuries resulting from traffic crashes. For the temporal factor represented by the four years interval, traffic crashes occurrences varied from district to district based on the level of injury.20222022-05-0120222022-07-27journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/371361reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3713612026-05-27T15:37:01Z
dc.title.none.fl_str_mv Traffic crash injuries occurrence varieties across Barcelona districts
title Traffic crash injuries occurrence varieties across Barcelona districts
spellingShingle Traffic crash injuries occurrence varieties across Barcelona districts
Aiash, Ahmad|||0000-0003-1941-4011
Traffic accidents
Barcelona
Districts
Injuries
Traffic crashes
Bayesian network
Accidents de trànsit
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport urbà
title_short Traffic crash injuries occurrence varieties across Barcelona districts
title_full Traffic crash injuries occurrence varieties across Barcelona districts
title_fullStr Traffic crash injuries occurrence varieties across Barcelona districts
title_full_unstemmed Traffic crash injuries occurrence varieties across Barcelona districts
title_sort Traffic crash injuries occurrence varieties across Barcelona districts
dc.creator.none.fl_str_mv Aiash, Ahmad|||0000-0003-1941-4011
Robusté Antón, Francesc|||0000-0001-9433-5386
author Aiash, Ahmad|||0000-0003-1941-4011
author_facet Aiash, Ahmad|||0000-0003-1941-4011
Robusté Antón, Francesc|||0000-0001-9433-5386
author_role author
author2 Robusté Antón, Francesc|||0000-0001-9433-5386
author2_role author
dc.subject.none.fl_str_mv Traffic accidents
Barcelona
Districts
Injuries
Traffic crashes
Bayesian network
Accidents de trànsit
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport urbà
topic Traffic accidents
Barcelona
Districts
Injuries
Traffic crashes
Bayesian network
Accidents de trànsit
Àrees temàtiques de la UPC::Enginyeria civil::Infraestructures i modelització dels transports::Transport urbà
description Barcelona (Spain) had a total population of 1,636,762 in 2019 that are distributed on 10 districts across the city. These districts have different characteristics in size and population density. As a result, this can lead to different traffic crashes injuries occurrences and numbers in these areas. Therefore, this study is attempting to determine the conditional probabilities for each district in order to identify the district that has highest number of injuries compared to other areas. A Bayesian network approach is utilized to analyze the dataset and identify the high-risk district alongside analyzing the varieties of traffic crashes during four-year intervals. The results have shown that the district that has the highest population, highest usage of private transport mode, and highest density of passenger cars per km2 (compared to all other districts), has the highest risk of having all types of injuries resulting from traffic crashes. For the temporal factor represented by the four years interval, traffic crashes occurrences varied from district to district based on the level of injury.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-05-01
2022
2022-07-27
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/371361
url https://hdl.handle.net/2117/371361
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-ShareAlike 4.0 International
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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