The traditional road traffic accident forecast mainly uses the historical data, including the number and the loss of traffic accidents, to predict the future trend, however, the traditional method can not reflect the relationship between the traffic accident and real-time traffic characteristics, and it also can not prevent accidents effectively. In order to solve the problems above, a real-time traffic accident prediction method based on AdaBoost classifier was proposed. Firstly, the road traffic states were divided into normal conditions and dangerous conditions, and the real-time collection of traffic flow data was used as the characteristic variable to characterize the different states, so the real-time prediction problem could be converted to a classification problem. Secondly, the Probability Density Function (PDF) of traffic flow characteristics under the two conditions in different time scales were estimated by Parzen window nonparametric estimation method, and the estimated density function was analyzed by the separability criterion based on probability distribution, then the sample data with appropriate characteristic variable and time scale could be determined. Finally, the AdaBoost classifier was trained to classify different traffic conditions. The experimental results show that the correct ratio by using standard deviation of traffic flow characteristics to classify test samples is 7.9% higher than that by using average value. The former can reflect the differences of different traffic states better, and also get better classification results.