A hybrid model based on Kalman Filter and neutral network for traffic prediction

In this paper, a hybrid model based on Kalman Filter and Neural Network is introduced for traffic prediction to make our travel more convenient. The proposed model, taking both the real-time data and the historical data, can predict the link travel time in near future more accurately and thus increase the user service quality of APTS. The performance of evaluation is demonstrated on the real link travel time from Wenhui Bridge to Mingguang Bridge collected by mobile phone supporting GPS. Finally MAPE is used to calculate the prediction error and the result shows that the hybrid model performs well than both the two separate models. Based on our proposed model for traffic prediction, the APTS, which is one of the most important applications of ITS, would attract much more people to use the public transportation system and greatly reliever the burden of the urban traffic pressure.