30
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Vol.30, No.7
2010
7
Systems Engineering — Theory & Practice July, 2010
: 1000-6788(2010)07-1320-06
: TP391
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Data mining using in a novel traffic flow forecasting model
YAN Wei
1
, LIU Yun-gang
1
, WANG Gui-hua
1
,GAOQi
2
(1. Department of Transportation Engineering, Sc hool of Energy and Power Engineering, Shandong Universit y,
Ji’nan 250061, China; 2. School of Mechanical Engineering, Shandong University, Ji’nan 250061, China)
Abstract A novel traffic flow forecasting model is presented in this paper. Firstly, because of fuzzy
C-means algorithm (FCM) is unsatisfied in circulating precision and speed. To improve the performances
of FCM algorithm, the fuzzy membership function and clustering centers are initialized by ant colony
algorithm. Then the practical historical data within one year is divided into several groups by ant colony-
fuzzy clustering algorithm. A separate module based on momentum BP neural networks models each
group. Using data from an important road in Ji’nan city, the satisfactory accurate results are obtained on
weekends and holidays. Moreover, the model is robust, and produces accurate results in some special cases
(raining or snowing).
Keywords ant colony algorithm; fuzzy clustering analysis; momentum BP neural networks; traffic flow
forecasting
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