Autor(es): Infante, Paulo ; Jacinto, Gonçalo ; Afonso, Anabela ; Rego, Leonor ; Nogueira, Pedro ; Silva, Marcelo ; Nogueira, Vitor ; Saias, José ; Quaresma, Paulo ; Santos, Daniel ; Gois, Patricia ; Rebelo Manuel, Paulo
Data: 2024
Identificador Persistente: http://hdl.handle.net/10174/37508
Origem: Repositório Científico da Universidade de Évora
Assunto(s): imbalance data; machine learning algorithms; multinomial logit model; ROSE technique; type of road traffic accident
Road traffic accidents (RTAs) are a problem with repercussions in several dimensions: social, economic, health, justice, and security. Data science plays an important role in its explanation and prediction. One of the main objectives of RTA data analysis is to identify the main factors associated with a RTA. The present study aims to contribute to the identification of the determinants for the type of RTA: collision, crash, or pedestrian running-over. These factors are essential for identifying specific countermeasures because there is a relevant relationship between the type of RTA and its severity. Daily RTA data from 2016 to 2019 in a district of Portugal were analyzed. A statistical multinomial logit model was fitted. The identified determinants for the type of RTA were geographical (municipality, location, and parking areas), meteorological (air temperature and weather), time of the day (hour, day of the week, and month), driver’s characteristics (gender and age), vehicle’s features (type and age) and road characteristics (road layout and type). The multinomial model results were compared with several machine learning algorithms, since the original data of the type of RTA are severely imbalanced. All models showed poor performance. However, when combining these models with ROSE for class balancing, their performance
