Title: Radial basis function neural network-based model predictive control for freeway traffic systems
Authors: Wang Dongli, Zhou Yan, He Xiaoyang
Addresses: Glorious Sun School of Business Administration, Donghua University, Shanghai 20051, China. ' Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China. ' College of Electric Engineering, Guangxi University, Nanning 530004, China
Abstract: A method of Radial Basis Function neural network-based Model Predictive Control (RBF-MPC) for freeway traffic systems is proposed in this paper. Because of nonlinearity and uncertainty of freeway traffic flow, an accurate mathematics model cannot be obtained. Therefore, RBF neural networks employed to predict the future behaviours of freeway traffic flow are designed based on the MATLAB neural network toolbox. Then, to handle nonlinearity, time delay, uncertainty and strong disturbance, RBF-MPC for ramp metering is proposed. A Genetic Algorithm (GA) is used in the receding horizon optimisation. Compared with the no-control case and optimal-control case, the simulation results demonstrate that the proposed approach can alleviate traffic jams and increase main road capacity; thus the efficiency of freeway traffic is improved tremendously.
Keywords: ramp metering; freeway traffic control; radial basis function; RBF neural networks; model predictive control; MPC; genetic algorithms; traffic flow; nonlinearity; time delay; uncertainty; disturbance; simulation; traffic jams; traffic congestion; main road capacity.
International Journal of Intelligent Systems Technologies and Applications, 2007 Vol.2 No.4, pp.370 - 388
Published online: 12 Jun 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article