Data Mining technique: Application of Apriori algorithm for road accident analysis

Ryan Clifford Larraquel Perez ()
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Ryan Clifford Larraquel Perez: Marinduque State College, Tanza, Boac, Marinduque

HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, 2023, vol. 13, issue 2, 60-68

Abstract: Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.

Keywords: apriori; association rule analysis; CRISP-DM; data mining; road accidents (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bjw:techen:v:13:y:2023:i:2:p:60-68

DOI: 10.46223/HCMCOUJS.tech.en.13.2.2831.2023

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