Traffic Risk- Safety Restraints- Awareness through Data Mining Approaches
M. Rekha Sundari1, P.V.G.D. Prasad Reddy2, Y. Srinivas3

1M. Rekha Sundari, ANITS, Visakhapatnam Andhra Pradesh, India.
2P.V.G.D. Prasad Reddy, Andhra University, Visakhapatnam, Andhra Pradesh, India.
3Y. Srinivas, GITAM University, Visakhapatnam , Andhra Pradesh, India.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2608-2613 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7643068519/19©BEIESP
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Abstract: Fatality in Road Traffic Injuries (RTI’s) has been a high burden in India. Fatality rates can be affected by many factors such as types of vehicles driven, travel speeds, rates of licensure, state traffic laws, weather, and topography. Accidents can be predicted, avoided and can occur without the notice of the individual. However may be the occurrence of the accident, prevention of the fatality is at the individual risk most of the times. Surveys of RTI state that use of restraints will mostly prevent the rate of fatality in accidents. Large proportions of these RTI include Motor vehicles and mostly motor cyclists. This paper highlights the role of restraint use in reducing fatality, using Data mining approaches. Initially the personnel data is classified with two labels: Fatality and Survival using legacy classification model like Decision tree classifier. A hybrid method for classification that constructs a decision tree using Association rules is proposed. The experimental results prove that the proposed method provides better accuracy when compared to legacy methods.
Keywords: Association Rule Mining, Accidents, Decision Tree, Fatality Rate.

Scope of the Article: Data Mining