As per the 2018 MoRTH accident report, there were 467,044
accidents, out of which 137,726 were fatal which resulted in
151,417 fatalities. In order to get an idea of the reasons for
injuries and estimate the benefits of any intervention, a
mathematical model should go a long way. This study is aimed at the
development of such a model to predict the injuries sustained by
the occupants of an M1 vehicle. We used a detailed accident
database of 'Road Accident Sampling System India' (RASSI).
RASSI, since 2011, has been collecting traffic accident data
scientific across various locations in India. In the data, the
occupant injuries are classified as No injury, Minor, Serious and
Fatal We used the data of about 4700+ M1 occupants for the study
& used almost 40 input parameters to determine the outcome.
Based on the data, an algorithm was developed with an overall
accuracy of about 67%. The parameters represented human,
infrastructure, and environment. In 67% of the cases, the injuries
were accurately predicted. In 14 % of the cases the predicted
injuries were one level above than actual i.e. for example in case
the actual injury was minor the model predicted it as serious we
term this as +1 shift error. Likewise, 11% of the time the model
predicted injury one level lower than the actual i.e. for example
if the actual injury was of a serious nature, the model predicted
it as minor. These can be termed as -1 shift errors. But if we
combine ±1 shift errors and the 0 errors the accuracy increases to
92%. The model can be used as a first step towards accessing the
effectiveness of an intervention. Post this more expensive field
trials may be carried out