Traffic Flow Combination Forecasting Based on Grey Model and GRNN
This paper focuses on traffic flow forecasting which is an essential component in traffic control or route guidance system. A combination forecasting model called GM-GRNN based on GM(1,1) and GRNN is built for short-term traffic flow time series. The basic theory and features of General Regression Neural Network (GRNN) and its advantages are introduced. The weight of combination model is determined by optimal combination method. In the GRNN model, the number of input neurons and the value of smooth factor are determined by search method, and the forecasting process is single-step rolling forecasting. The results demonstrate that the GM-GRNN model with the advantage of all single models accurately fits the actual traffic flow, and has better performance than single model.
Traffic Flow Combination Forecasting GM GRNN
Xianyan Kuang Cuiqin Wu Yanguo Huang Lunhui Xu
School of Mechanical and Electronic Engineering Jiangxi University of Science and Technology Ganzhou, Jiangxi, 341000, China
国际会议
长沙
英文
3434-3437
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）