Traffic accidents are a major concern worldwide, since they have a significant impact on
people's safety, health, and well-being, and thus, they constitute an important field of
research on the use of state-of-the-art techniques and algorithms to analyze and predict
them. The study of traffic accidents has been conducted using the information published by
traffic entities and road police forces, but thanks to the ubiquity and availability of social
media platforms, it is possible to have detailed and real-time information about road …

We develop an enhanced accident occurrence prediction model which depends on the
heterogeneous ensemble learning to tackle the topic of a accident period prediction in the
early stages of the tragedy using millions of the traffic accident information's from the India. In
order to start with, we concentrate on the early stages of development of accidents and
choose few useful data from five categories: location, the traffic, climate, objects, and the
time field. Further, we implement data cleansing, processing of outlier, and the missing value …