Short-term Traffic Flow Prediction Based on GM(1,1,Exp) Model

摘要：

In this paper, we use the gray model through the consolidation of original data to find the changing laws of the system, to generate a strong regularity of the data series, thus well predict the changing trend of the future.Meanwhile, considered the defect that the grey action of traditional GM(1,1) model is constant, we introduce a sine function relation and make the grey action into a dynamic variable which contains time function.Thus, the traditional GM(1,1) model is optimized.Finally, the result shows that the optimized GM(1,1) model can simulate better, and has a higher accuracy.

关键词： short-term traffic flow prediction GM(1 1 exp) model

作者: Mao Shuhua Chen Ye Xiao Xinping

作者单位: College of science Wuhan University of Technology Wuhan ,China

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Grey System and Intelligent Services Joint with the 15th WOSC International Congress on Cybernetics and System(2011 IEEE灰色系统与智能服务国际会议暨系统与控制世界组织第15届年会)

会议地点: 南京

会议语种:英文

页码: 275-279

在线出版日期: 2011-09-15（万方平台首次上网日期，不代表论文的发表时间）