Review previous studies of machine learning techniques to analyze road traffic accidents in different countries

RTAs are considered public health issues. For that, many countries and organizations, such as the Gulf Cooperation Council and United Nations, are trying to reduce the number of accidents caused by road traffic. This study reviews several previous studies that used machine learning algorithms to fin...

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Bibliographic Details
Published in2023 4th International Conference on Data Analytics for Business and Industry (ICDABI) pp. 359 - 364
Main Authors Alhaddad, Salman, Zeki, Ahmed M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.10.2023
Subjects
Environmental Factors
Human Factors
Machine Learning
Machine learning algorithms
Prediction algorithms
Radio frequency
Reviews
Road traffic
Road Traffic Accidents
Sensitivity
Support vector machines
Vehicle Factors
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Summary:RTAs are considered public health issues. For that, many countries and organizations, such as the Gulf Cooperation Council and United Nations, are trying to reduce the number of accidents caused by road traffic. This study reviews several previous studies that used machine learning algorithms to find the best models that can deal with the dataset related to RTAs, it is found the results differentiated by changing the dataset, some high results achieved with the NB algorithm, and the others achieved by using MLP and DT. This study includes more than 30 studies with more than 15 machine learning algorithms. There are several factors that affect the RTAs such as gender driver, age, type of vehicle, cause of accident, and lighting condition. The high number of algorithms used in the previous studies discussed during this study are DT, followed by RF, SVM, and NB respectively. This study provides a review to help researchers to understand the techniques used to analyze the dataset in the RTA sector.
DOI:10.1109/ICDABI60145.2023.10629593