An Intelligent Hybrid Forecasting Model for Short-term Traffic Flow

被引:4
作者
Shen Guo-jiang [1 ]
机构
[1] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310003, Zhejiang, Peoples R China
来源
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词
Short-term traffic flow; Forecasting; History mean model; Artificial neural network; Fuzzy logic; NEURAL-NETWORKS;
D O I
10.1109/WCICA.2010.5553786
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
According to the thought of intelligent forecasting and hybrid forecasting, an Intelligent Hybrid (IH) model for short-term traffic flow forecasting was presented. The IH model had three sub-models: History Mean (HM) model, Artificial Neural Network (ANN) model and the Fuzzy Combination (FC) model. By means of the good static stabilization character of HM method, the HM model predicted the traffic flow by the Single Exponential Smoothing method based on the historical traffic data. Otherwise, the ANN model was a 1.5-layer feed-forward neural network built by some common S-function neurons. Because of the strong dynamic nonlinear mapping ability of ANN, the ANN model can estimate the actual traffic flow in a very precise and satisfactory sense. The FC model mixed the two individual forecasting results by fuzzy logic and its output was regarded as the final forecasting of the traffic flow. Factual application results show that the IH model, which takes advantage of the unique strength of the HM model and the ANN model, can produce more precise forecasting than that of two individual models. Thus, the IH model can be an efficient method to the short-term traffic flow forecasting.
引用
收藏
页码:486 / 491
页数:6
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