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.
Kalman filter model Elman neural network model Link travel time APTS MAPE
Jianying Liu Wendong Wang Xiangyang Gong Xirong Que Hao Yang
Beijing University of Posts and Telecommunications,Beijing 100876,China
国际会议
杭州
英文
705-708
2012-10-30（万方平台首次上网日期，不代表论文的发表时间）