The Performance of Accident Severity Multiclass Classification
One way to monitor accidents on highway is to analyze the accident characteristic to predict the accident severity. This study applied multinomial logistic regression model to predict accident severity. Predicted accident severities are compared with actual accident severities to evaluate the pre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2022
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1578/ http://eprints.intimal.edu.my/1578/1/joit2022_06r.pdf http://eprints.intimal.edu.my/1578/2/291 |
| Summary: | One way to monitor accidents on highway is to analyze the accident characteristic to
predict the accident severity. This study applied multinomial logistic regression model to predict
accident severity. Predicted accident severities are compared with actual accident severities to
evaluate the prediction performances of the model. The aim of this study is to determine the
performance of accident severity classifications by multinomial logistic regression model. The
predicted accident severities could be used to estimate potential effect of changes in factors
contributing to accidents. Data was obtained from the Malaysian Highway Authority for the
year 2013 and 2014. The accident severity was grouped into four categories of death, serious
injury, minor injury and damage. Based on the results, the model correctly classified accident
severities by 63.52% using training data and 61.45% using validation data. The Hosmer-
Lemeshow test indicated the model has a good fit between the actual accident severities and
predicted accident severities and the ROC results indicted the model able to distinguish between
the classifications. The classifier of the model inclined more toward the damages compared to
other accident severities resulted in classifying accident severity classes with more samples
better and remains weak on the accident severity classes with lesser samples. |
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