NONLINEAR FORECASTING OF DAILY TRAFFIC FLOW BASED ON OPTIMAL EMBEDDING PHASE-SPACE
Traffic flow prediction is an important application in ITS. This paper presents a new method to build a nonlinear forecasting model for daily traffic flow prediction. The method consists of three steps. First, a statistic is offered to determine whether a linear model or a nonlinear model is suitable for a given time series. Second, if a nonlinear model is suitable, then a new algorithm is approved to synchronously select the optimal embedding dimension and delay step of the time series constructed phase-space. Last, a local linear forecasting model based on the optimal embedding phase-space is build. The real daily traffic flow data are applied to test the new method.
Time series Phase-space embedding Traffic flow forecasting Nonlinear
HONG XIE ZHONG-HUA LIU HONG-QIONG HUANG
College of Information Engineering, Shanghai Maritime University, Shanghai, 200135, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1341-1346
2008-07-12(万方平台首次上网日期,不代表论文的发表时间)