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Association rule mining on five years of motor vehicle crashes

Authors :
Satish Chilkaka
Khaled Bashir Shaban
Abdunnaser Younes
Jean Raymond Daher
Source :
MATEC Web of Conferences, Vol 81, p 02017 (2016)
Publication Year :
2016
Publisher :
EDP Sciences, 2016.

Abstract

Every year, road accidents kill more than a million people and injure more than 20 million worldwide. This paper aims to offer guidance on road safety and create awareness by pinpointing the major causes of traffic accidents. The study investigates motor vehicle crashes in the Genesee Finger Lakes Region of New York State. Frequency Pattern Growth algorithm is utilized to cultivate knowledge and create association rules to highlight the time and environment settings that cause the most catastrophic crashes. This knowledge can be used to warn drivers about the dangers of accidents, and how the consequences are worse given a specific context. For instance, a discovered rule from the data states that 'most of the crashes occur between 12:00 pm and 6:00pm'; hence, it is suggested to modify existing navigation application to warn drivers about the increase in risk factor. Scopus

Details

Language :
English
Database :
OpenAIRE
Journal :
MATEC Web of Conferences, Vol 81, p 02017 (2016)
Accession number :
edsair.doi.dedup.....1530ca0f1f383bf2e759b055338604c0