Data mining techniques along with fuzzy logic control to find solutions to road traffic accidents: case study in Morocco

Halima Drissi Touzani, Sanaa Faquir and Ali Yahyaouy

International Journal of Data Mining, Modelling and Management, 2024, vol. 16, issue 3, 344-357

Abstract: Collecting data on road accidents is important. However, it is equally important to analyse and process this data to prevent future accidents. Data analysis can provide valuable insights and help identify patterns, contributing to the development of effective strategies and interventions to improve road safety. Over years, many efforts in research have tackled several causes related to traffic accidents trying to identify risk factors. Different statistics identified that most accidents are due to human errors. In Morocco, a lot of studies have been applied to cars system to become automatic or semi-automatic to avoid serious injuries due to poor driving practices. This paper presents data mining techniques applied on real traffic accidents data using statistical analysis, K-means clustering algorithm and fuzzy logic. The data represents accidents that happened in Morocco during 2014. Results showed important features that caused previous accidents which was used to implement an algorithm based on fuzzy logic to train a semi-autonomous car to make right decisions whenever needed and therefore, prevent accidents from happening.

Keywords: data analysis; data mining techniques; road traffic accidents; semi-autonomous cars; fuzzy logic control; decision algorithm; statistical methods; Morocco. (search for similar items in EconPapers)
Date: 2024
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