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Road Traffic Accidents (RTA) cause human losses and irreparable physical and psychological damage to many of the victims. They also involve a very relevant economic dimension. It is urgent to improve the management of human and material resources for more effective prevention. This work makes an important contribution by presenting a methodology that allowed for achieving a predictive model for the occurrence of RTA on a road with a high RTA rate. The prediction is obtained for each road segment for a given time and day and combines results from statistical methods, spatial analysis, and artificial intelligence models. The performance of three Machine Learning (ML) models (Random Forest, C5.0 and Logistic Regression) is compared using different approaches for imbalanced data (random sampling, directional sampling, and Random Over-Sampling Examples (ROSE)) and using different segment lengths (500 m and 2000 m). This study used RTA data from 2016–2019 (training) and from May 2021–June 2022 (test). The most effective model was an ML logistic regression with the ROSE approach, using segments length 500 m (sensitivity = 87%, specificity = 60%, AUC = 0.82). The model was implemented in a digital application, and a Portuguese security force is already using it. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a major concern for modern society with a high toll on human life and involve hard to account economic consequences. New knowledge can be obtained from combining GIS tools with machine learning and artificial intelligence, developing what is, in this work, identified as spatial intelligence. This approach is tested in a case study of Setúbal district, Portugal, for the period of 2016 to 2019. Departing from a heatmap analysis, and applying kernel density estimation, new spatial approaches were used, namely DBSCAN and Getis-Ord. The results obtained allowed the identification of novel meaningful locations of road traffic accidents. Consequently, the knowledge built from the underlying patterns is considered the key to developing new strategies to solve this modern social curse. The methodology proposed in this study demonstrates that the combination of expertise built from the different spatial analyses can provide a better understanding of the determinants of road traffic accidents. This approach is expected to be valuable for data analysts and decision-makers, contributing to diminishing human losses related to road traffic accidents. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]