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      Neural network and clustering techniques for tractor accidents on highways in the south-east of Brazil Translated title: Rede neural e técnicas de agrupamento em acidentes com tratores em rodovias na Região Sudeste


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          ABSTRACT Until recently, accident indicators were analysed separately due to the methods employed, however, the joint use of neural networks and clustering techniques has proven to be an excellent tool for analysing how accidents occur. As such, the aim of this study was to use neural networks and cluster analysis on accident indicators involving tractors on federal highways in the south-east of Brazil. A total of 496 incidents were analysed between 2007 and 2016. The indicators for the accidents under evaluation were time, type of accident, cause of accident, weather conditions, condition of the victims, road layout and federated state. The use of neural networks was based on self-organising maps (SOM), hierarchical clustering employing dendrograms, and non-hierarchical clustering employing the k-means coefficient. Using these techniques, it was possible to divide the incidents into 18 accident groups, of which 11 were represented by the state of Minas Gerais, one group where casualties were predominant, and one group with fatalities. It proved possible to analyse the factors that led to the accident, together with its consequences. Machine traffic during periods of low natural light on straight roads caused rear-end collisions, with casualties and fatalities

          Translated abstract

          RESUMO Anteriormente os acidentes tinham seus indicadores analisados separadamente devido os métodos utilizados, todavia o uso em conjunto de redes neurais e técnicas de agrupamento tem se mostrado uma excelente ferramenta de análise de situações de ocorrência de sinistros. Assim objetivou-se realizar o uso de redes neurais e análise de agrupamento sobre os indicadores dos acidentes com tratores nas rodovias federais na região Sudeste. Foram analisadas 496 ocorrências entre o período de 2007 a 2016. Os indicadores dos sinistros avaliados foram: horário, tipo de acidente, causa do acidente, condições climáticas, condições dos acidentados, traçado da via e unidade federativa. O uso das redes neurais se deu pelos mapas auto-organizados-SOM, os métodos de agrupamento hierárquico por dendrograma e o não hierárquico pelo coeficiente de k-means. Através das técnicas foi possível dividir as ocorrências em 18 grupos de acidentes, dos quais 11 foram representados pelo estado de Minas Gerais, 1 grupo com dominância de vítimas feridas e 1 grupo com vítimas fatais. Foi possível analisar os fatores em conjunto que levaram a ocorrência dos sinistros e a consequência do mesmo. O tráfego de máquinas em períodos com pouca luminosidade natural em pistas retas, ocasionaram colisões traseiras com vítimas feridas e vítimas fatais.

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          Most cited references21

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          Analyzing fault and severity in pedestrian-motor vehicle accidents in China.

          The number of pedestrian-motor vehicle accidents and pedestrian deaths in China surged in recent years. However, a large scale empirical research on pedestrian traffic crashes in China is lacking. In this study, we identify significant risk factors associated with fault and severity in pedestrian-motor vehicle accidents. Risk factors in several different dimensions, including pedestrian, driver, vehicle, road and environmental factors, are considered. We analyze 6967 pedestrian traffic accident reports for the period 2006-2010 in Guangdong Province, China. These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report. Pedestrian traffic crashes have a unique inevitability and particular high risk, due to pedestrians' fragility, slow movement and lack of lighting equipment. The empirical analysis of the present study has the following policy implications. First, traffic crashes in which pedestrians are at fault are more likely to cause serious injuries or death, suggesting that relevant agencies should pay attention to measures that prevent pedestrians from violating traffic rules. Second, both the attention to elderly pedestrians, male and experienced drivers, the penalty to drunk driving, speeding, driving without a driver's license and other violation behaviors should be strengthened. Third, vehicle safety inspections and safety training sessions for truck drivers should be reinforced. Fourth, improving the road conditions and road lighting at night are important measures in reducing the probability of accident casualties. Fifth, specific road safety campaigns in rural areas, and education programs especially for young children and teens should be developed and promoted. Moreover, we reveal a country-specific factor, hukou, which has significant effect on the severity in pedestrian accidents due to the discrepancy in the level of social insurance/security, suggesting that equal social security level among urban and rural people should be set up. In addition, establishing a comprehensive liability distribution system for non-urban areas and roadways will be conducive to both pedestrians' and drivers' voluntary compliance with traffic rules.
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            Road traffic accident severity analysis: A census-based study in China

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              Essentials of the self-organizing map


                Author and article information

                Revista Ciência Agronômica
                Rev. Ciênc. Agron.
                Universidade Federal do Ceará (Fortaleza, CE, Brazil )
                : 52
                : 4
                [3] Fortaleza CE orgnameInstituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE) orgdiv1Departamento de Telemática orgdiv2Programa de Pós Graduação em Engenharia em Telecomunicações (PPGET) Brasil wally@ 123456ifce.edu.br
                [2] Fortaleza CE orgnameUniversidade Federal do Ceará (UFC), Programa de Pós Graduação em Engenharia Agrícola (DENA) orgdiv1Departamento de Engenharia Agrícola orgdiv2Centro de Ciências Agrárias (CCA) Brasil derilsiqueira@ 123456hotmail.com
                S1806-66902021000400407 S1806-6690(21)05200400407

                This work is licensed under a Creative Commons Attribution 4.0 International License.

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