Travel Time Estimating Algorithms Based On Fuzzy Radial Basis Function Neural Networks
Travel time estimating is one of main content in Intelligent Transportation System(ITS), accurate travel time estimating is also crucial application in the route guidance and advanced traveler information systems. In this paper, travel tine estimating algorithm based on Fuzzy Radial Basis Function Neural networks (FRBFNN) is proposed, the neural networks input is currently traffic flow and occupancy ratio of the road segment. A gradient descend learning algorithm with a momentum factor in this network model is introduced to decide the parameters of RBF in the membership function layer, and the output layer weight. Real road experiment results have shown that the proposed travel time estimating algorithm is feasible. Comparing with traditional method, the estimating error, both relative mean errors and root-mean-squared errors of travel times, is reduced significantly.
ITS Fuzzy RBF Neural Networks Travel time estimating
Haibin Su Yingzhan Hu Junhong Xu
Electric Power School, North China University of Water Conservancy and Electric Power, Zhengzhou, 45 Electric Engineering Department , Henan Polytechnic Institute, Nanyang, Henan Province, 473009, Chin
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
2653-2656
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）