Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety
This study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to crashes. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/5/117 |
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| author | Carlos Fabricio Assunção da Silva Mauricio Oliveira de Andrade Cintia Campos Alex Mota dos Santos Hélio da Silva Queiroz Júnior Viviane Adriano Falcão |
| author_facet | Carlos Fabricio Assunção da Silva Mauricio Oliveira de Andrade Cintia Campos Alex Mota dos Santos Hélio da Silva Queiroz Júnior Viviane Adriano Falcão |
| author_sort | Carlos Fabricio Assunção da Silva |
| collection | DOAJ |
| description | This study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to crashes. The number of fatalities on public roadways was then modeled using a multilayer perceptron artificial neural network employing the significant variables as predictors according to the generalization capacity of complex predictive models. The OLS and TOBIT findings indicated that the variables motorcycles and scooters per capita, municipal human development index, and number of SUS emergency units were the most important for modeling traffic fatalities at the scene at the national and regional levels. Applying these variables, the neural network’s best results achieved a hit rate of 88% for Brazil and 95% for the Northeast model. The contribution of this study is providing an approach combining various methods and considering a range of variables influencing traffic fatalities at the scene. The findings offer insights for policymakers, researchers, and practitioners involved in road safety initiatives, mainly where crash data are scarce, and macro-level analysis is necessary. |
| format | Article |
| id | doaj-art-69c38ba5d4e8472c98790a8e1e3d5462 |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-69c38ba5d4e8472c98790a8e1e3d54622025-08-20T03:47:59ZengMDPI AGInfrastructures2412-38112025-05-0110511710.3390/infrastructures10050117Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road SafetyCarlos Fabricio Assunção da Silva0Mauricio Oliveira de Andrade1Cintia Campos2Alex Mota dos Santos3Hélio da Silva Queiroz Júnior4Viviane Adriano Falcão5Department of Cartographic Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Avenida Acadêmico Hélio Ramos, Cidade Universitária, s/n, Recife 50740-530, BrazilDepartment of Civil and Environmental Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Avenida da Engenharia, s/n-Cidade Universitária, Recife-Pernambuco 50670-420, BrazilFaculty of Science and Technology, Federal University of Goiás, Estrada Municipal, Lote 04, Sala 425, Fazenda Santo Antônio, Aparecida de Goiânia 74971-451, BrazilCenter of Agroforestry Sciences and Technologies, Federal University of Southern Bahia, Rodovia Ilhéus/Itabuna, Km 22, Itabuna 45604-811, BrazilDepartment of Civil and Environmental Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Avenida da Engenharia, s/n-Cidade Universitária, Recife-Pernambuco 50670-420, BrazilDepartment of Civil and Environmental Engineering, Center of Technologies and Geosciences, Federal University of Pernambuco, UFPE, Avenida da Engenharia, s/n-Cidade Universitária, Recife-Pernambuco 50670-420, BrazilThis study applied 2019 macro-level data from DATASUS to model traffic fatalities at the scene. Ordinary least squares (OLS) and censored regression models (TOBIT) were the methodologies used to identify the significant variables explaining the occurrence of deaths on public roads due to crashes. The number of fatalities on public roadways was then modeled using a multilayer perceptron artificial neural network employing the significant variables as predictors according to the generalization capacity of complex predictive models. The OLS and TOBIT findings indicated that the variables motorcycles and scooters per capita, municipal human development index, and number of SUS emergency units were the most important for modeling traffic fatalities at the scene at the national and regional levels. Applying these variables, the neural network’s best results achieved a hit rate of 88% for Brazil and 95% for the Northeast model. The contribution of this study is providing an approach combining various methods and considering a range of variables influencing traffic fatalities at the scene. The findings offer insights for policymakers, researchers, and practitioners involved in road safety initiatives, mainly where crash data are scarce, and macro-level analysis is necessary.https://www.mdpi.com/2412-3811/10/5/117traffic safetytraffic crash modelingtraffic managementtraffic fatalitiesmultiple linear regression |
| spellingShingle | Carlos Fabricio Assunção da Silva Mauricio Oliveira de Andrade Cintia Campos Alex Mota dos Santos Hélio da Silva Queiroz Júnior Viviane Adriano Falcão Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety Infrastructures traffic safety traffic crash modeling traffic management traffic fatalities multiple linear regression |
| title | Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety |
| title_full | Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety |
| title_fullStr | Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety |
| title_full_unstemmed | Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety |
| title_short | Macro-Level Modeling of Traffic Crash Fatalities at the Scene: Insights for Road Safety |
| title_sort | macro level modeling of traffic crash fatalities at the scene insights for road safety |
| topic | traffic safety traffic crash modeling traffic management traffic fatalities multiple linear regression |
| url | https://www.mdpi.com/2412-3811/10/5/117 |
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