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This study focused on comparing the classification performance and accuracy for short-term urban traffic flow condition using decision tree algorithms (CHAID, CART, QUEST and C5.0). In building decision tree models, input variables were multiple roads' traffic flow condition value at current time, while, target variable was a certain road's condition value at future temporal horizon from 5-30 min. The results showed that when all the predictors were input without feature selection, the classification accuracy obtained by CART algorithm was higher than the other three algorithms. While using CART and CHAID with feature selection , the accuracy showed lower but the obtained decision tree expressed more concise and understandable with fewer nodes, besides, by enlarging training samples to about 10 times of that before , the accuracy with feature selection is higher than that without feature selection.