Accident Analysis Using Machine Learning

EasyChair Preprint no. 9909

Abstract

The main aim of this paper is to analyze the

road accidents in India at national, state, and metropolitan city

level. Analysis shows that the distribution of road accidental

deaths and injuries in India varies according to age, gender,

month and time. Age group 30- 59 years is the most vulnerable

population group, though males face higher level of fatalities

and injuries than their female counterparts. Moreover, road

accidents are relatively higher in extreme weather and during

working hours. Analysis of road accident scenario at state and

city level shows that there is a huge variation in fatality risk

across states and cities. Fatality risk in 16 out of 35 states and

union territories is higher than the all India average. Although,

burden of road accidents in India is marginally lower in its

metropolitan cities, almost 50% of the cities face higher fatality

risk than their moffusil counterparts. In general, while in many

developed and developing countries including China, road

safety situation is generally improving, India faces a worsening

situation. Without increased efforts and new initiatives, the total

number of road traffic deaths in India is likely to crossthe mark

of 250,000 by the year 2025. There is thus an urgent need to

recognize the worsening situation in road deaths and injuries

and to take appropriate action.

Keyphrases: fatality rate, Fatality risk, public health, road safety

@Booklet{EasyChair:9909,
  author = {Sahil Verma and Anand Kumar},
  title = {Accident Analysis Using Machine Learning},
  howpublished = {EasyChair Preprint no. 9909},

  year = {EasyChair, 2023}}