Traffic risk-safety restraints-awareness through data mining approaches

ISSN: 22498958
1Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Rekha Sundari, M., Prasad Reddy, P. V. G. D., & Srinivas, Y. (2019). Traffic risk-safety restraints-awareness through data mining approaches. International Journal of Engineering and Advanced Technology, 8(5), 2608–2613.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free