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Title: Comparison of Statistical and Machine Learning Models on Road Traffic Accident Severity Classification
Authors: infante, paulo
Jacinto, Gonçalo
Afonso, Anabela
Rego, Leonor
Nogueira, Vitor
Quaresma, Paulo
Saias, José
Santos, Daniel
Nogueira, Pedro
Silva, Marcelo
Costa, Rosalina
Góis, Patrícia
Manuel, Paulo Rebelo
Keywords: injury
logistic regression
machine learning
road traffic accidents
severity of victims
Issue Date: 16-May-2022
Citation: 3. Infante, P., Jacinto, G., Afonso, A., Rego, L., Nogueira, V., Quaresma, P., Saias, J., Santos, D., Nogueira, P., Silva, M., Costa, R. P., Gois, P., Manuel, P. R. (2022). Comparison of Statistical and Machine Learning Models on Road Traffic Accident Severity Classification. Computers, 11, 80.
Abstract: Portugal has the sixth highest road fatality rate among European Union members. This is a problem of different dimensions with serious consequences in people’s lives. This study analyses daily data from police and government authorities on road traffic accidents that occurred between 2016 and 2019 in a district of Portugal. This paper looks for the determinants that contribute to the existence of victims in road traffic accidents, as well as the determinants for fatalities and/or serious injuries in accidents with victims. We use logistic regression models, and the results are compared to the machine-learning model results. For the severity model, where the response variable indicates whether only property damage or casualties resulted in the traffic accident, we used a large sample with a small imbalance. For the serious injuries model, where the response variable indicates whether or not there were victims with serious injuries and/or fatalities in the traffic accident with victims, we used a small sample with very imbalanced data. Empirical analysis supports the conclusion that, with a small sample of imbalanced data, machine-learning models generally do not perform better than statistical models; however, they perform similarly when the sample is large and has a small imbalance.
Type: article
Appears in Collections:CIMA - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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