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

Authors: Halima Drissi Touzani; Sanaa Faquir; Ali Yahyaouy

Addresses: LISAC Laboratory, Faculty of Science Dhar Mehraz, Sidi Mohamed Ben Abdallah University, Fez, Morocco ' LISAC Laboratory, Faculty of Science Dhar Mehraz, Sidi Mohamed Ben Abdallah University, Fez, Morocco; Laboratory of Intelligent Systems, Energy and Sustainable Development (LSIEDD), Faculty of Engineering Sciences, Private University of Fez, Fez, Morocco ' LISAC Laboratory, Faculty of Science Dhar Mehraz, Sidi Mohamed Ben Abdallah University, Fez, Morocco

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.

DOI: 10.1504/IJDMMM.2024.140542

International Journal of Data Mining, Modelling and Management, 2024 Vol.16 No.3, pp.344 - 357

Received: 16 Feb 2023
Accepted: 25 Nov 2023
Published online: 22 Aug 2024 *

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