Analyzing and Predicting Fatal Road Traffic Crash Severity Using Tree-Based Classification Algorithms

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 Chi-square Automatic Interaction Detector (CHAID), Classification and Regression Tree (CART), C4.5 and C5.0 decision tree data mining classification algorithms 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 results were evaluated using the accuracy metrics such as overall accuracy, kappa rate, precision, recall, and F-measure. The investigations confirmed that C5.0 had the best performance with the overall accuracy, and kappa rates of 94% and 92%, respectively. Additionally, classified fatality severity levels of the crashes were proposed for each algorithm to generate risk maps on the roads, to create potential accident risk spots. 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 policymakers.

Analyzing and Predicting Fatal Road Traffic Crash Severity Using Tree-Based Classification Algorithms Keywords:

Fatality Severity , Risk Map , Classification , Decision Tree algorithms