Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal

Paulo Infante (), Gonçalo Jacinto (), Anabela Afonso, Leonor Rego, Pedro Nogueira, Marcelo Silva, Vitor Nogueira, José Saias, Paulo Quaresma, Daniel Santos, Patrícia Góis and Paulo Rebelo Manuel
Additional contact information
Paulo Infante: CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
Gonçalo Jacinto: CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
Anabela Afonso: CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal
Leonor Rego: Department of Mathematics, ECT, University of Évora, 7000-671 Évora, Portugal
Pedro Nogueira: ICT, IIFA, University of Évora, 7000-671 Évora, Portugal
Marcelo Silva: ICT, IIFA, University of Évora, 7000-671 Évora, Portugal
Vitor Nogueira: Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
José Saias: Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
Paulo Quaresma: Algoritmi Research Centre, University of Évora, 7000-671 Évora, Portugal
Daniel Santos: Department of Informatics, ECT, University of Évora, 7000-671 Évora, Portugal
Patrícia Góis: Department of Visual Arts and Design, EA, University of Évora, 7000-208 Évora, Portugal
Paulo Rebelo Manuel: CIMA, IIFA, University of Évora, 7000-671 Évora, Portugal

Sustainability, 2023, vol. 15, issue 3, 1-16

Abstract: 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 improved considerably, with the random forest algorithm showing the best performance.

Keywords: imbalance data; machine learning algorithms; multinomial logit model; ROSE technique; type of road traffic accident (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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