A Machine Learning Approach for Classifying Road Accident Hotspots
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...
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| Published in | ISPRS international journal of geo-information Vol. 12; no. 6; p. 227 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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01.06.2023
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| Abstract | 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 road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%. |
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| AbstractList | 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 road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%. |
| Audience | Academic |
| Author | Baptista, Cláudio de Souza Amorim, Brunna de Sousa Pereira Firmino, Anderson Almeida Júnior, Geraldo Braz Júnior, Francisco Edeverton de Almeida Paiva, Anselmo Cardoso de |
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| SubjectTerms | 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 |
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| Title | A Machine Learning Approach for Classifying Road Accident Hotspots |
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