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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Bibliographic Details
Published inISPRS 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
LanguageEnglish
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
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Summary: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%.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi12060227