Abstract: In order to further improve the accuracy and efficiency of traffic incident detection ,this paper develops a traffic incident detection algorithm based on factor analysis (FA) and minimax probability machine (MPM) .The initial traffic variables were assigned from the multiple perspectives .This paper developed 11 initial traffic incident detection variables by analyzing the changes of upstream and downstream traffic flows .Factor analysis method is used for feature extraction ,and the dimension of initial traffic variables reduces effectively .The Kernel minimax probability machine and Linear minimax probability machine algorithm are used for traffic incident detection .The proposed method was tested with real traffic flow data from I‐880 database of USA .The experimental results demonstrate that the identification rate of FA‐M PM algorithm is 3 .5% higher than M PM algorithm ,the false identification rate decreases 0 .17% ,and the aver‐age detection time decreases 27 .5 s .The study concludes that MPM algorithm will provide better traffic incident detection than support vector machine (SVM) algorithm and BP neural network (BPNN) algorithm .