dor_id: 4110160

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510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/708/678

100.1.#.a: de Campos, Cintia Isabel; dos Santos, Murilo Castanho; Pitombo, Cira Souza

524.#.#.a: de Campos, Cintia Isabel, et al. (2018). Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm. Journal of Applied Research and Technology; Vol. 16 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/4110160

245.1.0.a: Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2018

264.#.1.c: 2019-06-20

653.#.#.a: Classification Rules; Decision Tree; Road Safety; Macro Level Data

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/708

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041.#.7.h: eng

520.3.#.a: Road traffic accidents occur daily caused by different factors leading to varying degrees of injury severity. Considering this, many studies have been developed to identify and understand these factors to implement preventive actions. A Decision Tree (DT) is one of the techniques that can generate classifications and predictions by detecting a priori unknown patterns. This study aims to identify the characteristics of municipalities with high and very high fatality rates caused by traffic accidents, using a macro level dataset and a DT algorithm (CART - Classification And Regression Tree). Therefore, macro level data from the municipalities of São Paulo state (Brazil) were used, such as demographic and socioeconomic data, fatality rates and other variables related to traffic. The results indicated the Gross Domestic Product (GDP) as the most important variable, and the municipalities were characterized mainly considering the size of the highway network and vehicle fleet (trucks, minibuses, cars, motorcycles). These characteristics could provide support to the government to plan mitigating actions in municipalities with the highest tendency to high traffic fatality rates.

773.1.#.t: Journal of Applied Research and Technology; Vol. 16 Núm. 2

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.16656423.2018.16.2.708

harvesting_date: 2023-11-08 13:10:00.0

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Artículo

Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm

de Campos, Cintia Isabel; dos Santos, Murilo Castanho; Pitombo, Cira Souza

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

de Campos, Cintia Isabel, et al. (2018). Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm. Journal of Applied Research and Technology; Vol. 16 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/4110160

Descripción del recurso

Autor(es)
de Campos, Cintia Isabel; dos Santos, Murilo Castanho; Pitombo, Cira Souza
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm
Fecha
2019-06-20
Resumen
Road traffic accidents occur daily caused by different factors leading to varying degrees of injury severity. Considering this, many studies have been developed to identify and understand these factors to implement preventive actions. A Decision Tree (DT) is one of the techniques that can generate classifications and predictions by detecting a priori unknown patterns. This study aims to identify the characteristics of municipalities with high and very high fatality rates caused by traffic accidents, using a macro level dataset and a DT algorithm (CART - Classification And Regression Tree). Therefore, macro level data from the municipalities of São Paulo state (Brazil) were used, such as demographic and socioeconomic data, fatality rates and other variables related to traffic. The results indicated the Gross Domestic Product (GDP) as the most important variable, and the municipalities were characterized mainly considering the size of the highway network and vehicle fleet (trucks, minibuses, cars, motorcycles). These characteristics could provide support to the government to plan mitigating actions in municipalities with the highest tendency to high traffic fatality rates.
Tema
Classification Rules; Decision Tree; Road Safety; Macro Level Data
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces