A Machine Learning Approach for Classifying Road Accident Hotspots

• Published in: ISPRS international journal of geo-information Vol. 12; no. 6; p. 227
• Main Authors: Amorim, Brunna de Sousa Pereira, Firmino, Anderson Almeida, Baptista, Cláudio de Souza, Júnior, Geraldo Braz, Paiva, Anselmo Cardoso de, Júnior, Francisco Edeverton de Almeida
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Accidents; Algorithms; Analysis; Classification; Data mining; Datasets; Decision trees; Fatalities; feature selection; highway accident; Hot spots; Learning algorithms; Machine learning; Multilayer perceptrons; Multilayers; Neural networks; Police; Prediction models; Risk; Risk factors; Roads; Roads & highways; supervised machine learning; Traffic accidents; Traffic accidents & safety; Vehicles; Weather; China; Brazil

Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of ro...