Road Traffic Accident Analysis and Prediction Model: A Case Study Of Kashmir
Waseem Akram Mir
M. tech (Scholar), Civil Department, RIMT University, Mandi Gobindgarh, Punjab, India , IN
Dr.V.S. Batra
M. tech (Scholar), Civil Department, RIMT University, Mandi Gobindgarh, Punjab, India , IN
Er.Sandeep Singla
M. tech (Scholar), Civil Department, RIMT University, Mandi Gobindgarh, Punjab, India , IN
Keywords: Accident data, accident severity, fatality rate, accident prediction model,traffic accidents
Abstract
Road traffic injuries are recognized globally as a major public health problem.In case of india, Road injury is one of the top four leading causes of death and health loss among persons of age group 15-49 years. As per my research point of view , the total number of road accidents are declined from past few years but the number of persons killed in road crash has increased over the previous years. Accident severity has increased consistently from last few years and shows a sharp rise . Actually the number of accident due to pot holes,road surface conditions, undivided road,width of roadway etc has been ended so far. In 2016-2017 in Kashmir division more than 80% accidents are caused due to hit and run . During the calendar year 2017, the total number of road accidents is reported at 1,748 causing 274 lives in Kashmir division. This would translate on an average into 5 accidents and one fatal accident taking place on daily basis. The number of fatal accidents is rising up every year due to increasing vehicles and population. To develop road accident prediction model each and every parameter related with the accident is considered and a micro level analysis of road accident is performed. For micro level analysis road traffic accident data of last seven year (2011 to 2017) from JK Traffic Police stations and vehicles registered details from Regional Traffic Officer Srinagar are collected and a detailed analysis is performed on that basis. On basis of this analysis effect of accident is identified. After analysis road traffic accident prediction models is developed based on
vehicular ownership to population (v/p) ratio.
How to Cite
References
NA