For aim applied to develop Intelligent Transportation System (ITS), a traffic incident detection method based on Reduced Error Pruning Tree (REPTree) algorithm of decision tree is presented. Different from unpruned decision tree, REPTree model is a fast decision tree learner which builds a decision tree using information gain as the splitting criterion, and prunes it using reduced-error pruning. The detection performance of the REPTree was compared to multi-layer feed forward neural networks (MLFNN) and Radical Basis Function neural networks (RBFNN) which yield superior incident detection performance in the previous studies. The experimental results indicate that REPTree model is competitive with MLFNN and RBFNN.