This study creates an abnormal traffic status alarming method based on the neural architecture. The paper introduces the three layer BP (Back Propagation) neural network structure including the input layer, the hidden layer and the output layer. Each layer of the network will receive the input information from the upper layer of the network after which the crunodes will change the information by means of the non-linear mapped. Therefore the changed information will be passed down to the next layer. Finally, there are several real examples on demonstrating the effectiveness of system algorithms. Thereby travelers and traffic management units can better understand the impact of the existing incident. Based on the model effect assessments, this study shows that the proposed models are feasible in the Intelligent Transportation Systems (ITS) context.