A Comparative Study of CART & C5.0 Classification Algorithms in Road Accident Severity Classification

Nowadays, a significant part of goods and passengers are transported on suburban highways with mainly high speed vehicles. Hence, these highways are very prone to accidents with different injuries. Due to the high fatality or severe physical/mental injury rates caused by car crashes, analyzing these accident-prone areas and identifying the factors affecting their occurrences is crucial. The specific objective of the study was to compare two decision trees, CART (Classification and Regression Tree) and C5.0 in building classification models for the fatality severity of 2355 fatal crash data records during 2007-2009 occurred in the roadways of 8 states in the USA. The investigations confirmed that C5.0 had a better performance than CART with a higher accuracy and kappa rates of 70% and 60%, respectively. Decision tree models can be used for real-time data to find invariants in the tree over a period of time, which would be beneficial for the policy makers.