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Association rule mining on five years of motor vehicle crashes
- 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
- Subjects :
- Engineering
Association rule learning
Crashworthiness
Context (language use)
Computer security
computer.software_genre
Association rules
Data state
Finger lakes
Frequency patterns
Operations management
business.industry
Highway engineering
Risk factor (computing)
Roads and streets
New York State
Motor transportation
Risk factors
lcsh:TA1-2040
Accidents
Road safety
Motor vehicle crashes
Highway accidents
business
lcsh:Engineering (General). Civil engineering (General)
computer
Motor vehicle crash
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- MATEC Web of Conferences, Vol 81, p 02017 (2016)
- Accession number :
- edsair.doi.dedup.....1530ca0f1f383bf2e759b055338604c0