Assessing the Predictive Abilities of Statistical Injury-Severity Prediction Modelling Considering non-behavioral Factors of Accident
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Date
2018-03-19
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AAU
Abstract
In the developing country, Road Traffic Accidents are among the leading cause of death
and injury; Ethiopia in particular experiences the highest rate of such accidents. Road
Traffic Accidents cannot be absolutely eradicated, however, it is possible to prevent them
to some extent as long as the contributing factors are identified and tackled appropriately.
The driver behavior plays a crucial role in the occurrence of a crash; but, it is usually
complex and unpredictable, and also focusing too much on the driver as the cause of a
crash often masked the ability to see other causes that could reduce crash rates and crash
severity. So it is important to figure out the role of the non-behavioral factors in traffic
accidents, based on which cost-effective countermeasures can be recommended to reduce
the chance of accidents. Previously, significant studies were undertaken to predict the
magnitude of road traffic accidents and black spot areas in Addis Ababa City considering
the driver as the main causing factor. However significant studies were not undertaken to
predict the magnitude of accident severity considering the non-behavioral factors as the
main causing factor. As a result, this study developed accident severity prediction models
using Multinomial logistic regression that link accident severity to non-behavioral
contributing factors. For this study, a total of 5251 traffic accident data from June 30/2011
to June 30/2016GC were collected from Yeka sub-city. Multinomial logistic regression
was used to estimate the model parameters. The data set obtained for this study were
applied to examine the goodness-of-fit regression models. The dependent variable used in
this study was crash severity. As part of the study, the models have been tested to see how
well they predict the accidents observed during a one year accident period. From the model
developed, road type, road surface condition, crash type, maneuvering condition and
lighting conditions were found as significant explanatory variables that influence the
prediction of crashes in the yeka sub-city. This indicates that non-behavioral factors have
an effect on the occurrence of accident.
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Keywords
Road traffic accident, Non-behavioral factors, Accident severity Prediction Models, Multinoial Logistic regression, , Crash Severity level