CONTACT US: Contact info
|
Citation |
Pasupuleti NS, Aggarwal S. Inroads Int. J. Jaipur National Univ. 2016; 5(1s): 211-214. |
|
Copyright |
(Copyright © 2016, Jaipur National University) |
|
DOI |
|
PMID |
unavailable |
|
Abstract |
Road traffic accidents are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, Traffic Accidents are among the leading cause of death and injury; India in particular experiences the highest rate of such accidents. According to Transport Research Wing, Ministry of Road Transport and Highways [3], road accidents in India, 2012, Between 1970 and 2011, the number of accidents increased 4.4 times accompanied with 9.8 times increase in fatalities and 7.3 times increase in the number of persons injured. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. For this to happen reliable data set is of high importance and there is a huge crisis of accident data in India, hampering safety promotion and remedial activities. In this particular research project, need for big data which is rapidly expanding in Transportation arena for better models, estimation, prediction and prevention to minimize traffic related crashes is discussed. It is high time for extensive data mining and recording techniques are which can boost the existing traffic accident data, for better understanding of high crash locations, reasons for crashes and how these locations can be made crash free for safer livelihood. Language: en |