Real Time Turning Flow Estimation Based on Model Predictive Control
In order to predict the real time turning flow at intersections, which is used for the real-time adaptive traffic signal control, a real time turning flow estimation model based on model predictive control is proposed. The model adopts multiple independent parallel BP neural networks to structure the prediction model in the model predictive control mechanism, which adequately exerts the advantages of roiling optimization, feedback correction, and multistep prediction. The benefit of this is to improve the prediction accuracy. We utilize the microscopic traffic simulator with mathematical software and proper computational applications for the simulation. The simulation results prove that real time turning flow estimation model based on model predictive control has been more effective, compared with the traditional neural network prediction model.
model predictive control microscopic simulation turning movement proportion neural network
Guozhen Tan Haiquan Hao Yaodong Wang
School of Computer Science and Technology Dalian University of Technology Dalian, China
国际会议
重庆
英文
356-360
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）