This study applies classification and regression tree (CRT) to identify the hidden knowledge in fatal accidents of motor vehicles from Fatal Traffic Accident of National Police Agency, Taiwan. In the beginning, twenty four variables are chosen from Fatal Traffic Accident data set. Later, dimension reduction is used to reduce the number of variables from twenty four to nine variables by principal component analysis. With two different CRT models with twenty four and nine variables to forecast injury severity, a comparison is made in terms of rules generated, model accuracy, type I and type II errors, and evaluation chart generated by IBM SPSS Modeler 14.2. The results show that the CRT model with dimension reduction outperforms the CRT model without dimension reduction almost in every category except for type II error since this model tends to slightly overestimate the injury severity of motor vehicle traffic accidents than the model without dimension reduction.
Digital Object Identifier (DOI)
Liu, Yu-Huei; Kou, Kuang-Yang; Wu, Hsin-Hung; and Nian, Ya-Chi
"Using Classification and Regression Tree and Dimension Reduction in Analyzing Motor Vehicle Traffic Accidents,"
Applied Mathematics & Information Sciences: Vol. 10:
2, Article 23.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol10/iss2/23