A Novel Framework to Use Association Rule Mining for classification of traffic accident severity

Meenu Gupta, Vijender Kumar Solanki, Vijay Kumar Singh

Resumen


Introduction Traffic accidents are an undesirable burden on the society. Every year around one million deaths and more than 10 million injuries are reported due to traffic accidents. Hence, traffic accidents prevention measures must be taken to overcome the accident rate. Different countries have different geographical and environmental conditions and hence the accident factors are different in each country. Traffic accident data analysis is very useful in revealing the factors that affects the accidents in different country. The article was written in the year 2016 in the Institute of Technology & Science, Mohan Nagar, Ghaziabad, UP, INDIA. Methology In this paper, we have proposed a framework to utilize association rule mining (ARM) for classification of severity of traffic accident data obtained from police records in Mujjafarnagar district, Uttarpradesh, India. Results: the results certainly reveal some hidden factors which can be utilized to understand the factors behind road accident in this region. . Conclusions: The framework enables us to find three clusters from the data set. Each cluster represents a type of accident severity, i.e. fatal, major injury and minor/no injury. The association rules exposed different factors that are associated with road accidents in each of the category. The information extracted provides important information which can be certainly utilized to put some preventive measures to overcome the accident severity in Muzzafarnagar district.


Palabras clave


Association rule mining; classification; traffic accident; severity analysis

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Referencias


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DOI: https://doi.org/10.16925/in.v13i21.1726

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