Citation

Alzyoud F. Int. J. Comput. Commun. Control 2023; 18(3): e5006.

Copyright

(Copyright © 2023, CCC Publications)

DOI

10.15837/ijccc.2023.3.5006

PMID

unavailable

Abstract

This paper addresses the challenge of the increasing number of traffic accidents resulting from population growth and increased vehicle usage, leading to significant economic and environmental impacts. To address this issue, this paper proposes an improved model for traffic accident management that employs Knowledge Discovery in Databases (KDD) and extensive data analysis techniques to extract the main factors contributing to car accidents. A case study of traffic accidents in Jordan is conducted, utilizing actual data from the Department of Statistics and the Public Security Directorate Records between 2016 to 2020 to identify the primary factors contributing to road accidents and their effects on various types of injuries. The study identifies 11 factors that contribute to car accidents, with driver error being the primary factor for increasing the number of accidents and injuries. Additionally, minor car accidents are found to cause more injuries based on the analysis of the proposed approach. The findings provide a solid foundation for developing an accurate and precise scheme to reduce or eliminate the number of road accidents, which is essential for transitioning to smart cities. Using big data and KDD techniques could significantly improve current traffic accident management practices, informing the development of new policies and regulations to improve road safety and reduce economic and environmental impacts. This paper presents a promising solution for addressing traffic accident management, contributing to safer and sustainable urban environments.

Language: en

Keywords

artificial intelligence; big data; Jordan; KDD; machine learning; road accidents; traffic management