NDLI: Short-term forecasting model of traffic flow based on GRNN

Content Provider	IEEE Xplore Digital Library
Author	Ziwen Leng Junwei Gao Yong Qin Xin Liu Jing Yin
Copyright Year	2013
Description	Author affiliation: Qingdao Hisense TransTech Co., Ltd., Qingdao, China (Xin Liu) \|\| State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China (Yong Qin) \|\| Coll. of Autom. Eng., Qingdao Univ., Qingdao, China (Ziwen Leng; Junwei Gao; Jing Yin)
Abstract	Urban traffic flow has the characteristics of nonlinearity and time-variation, and how to accurately forecast short-term traffic flow has been an essential part in traffic field. Taking advantage of the Generalized Regression Neural Network (GRNN), the paper establishes the short-term forecasting model of traffic flow based on GRNN. The GRNN model selects the cross validation algorithm to train the network, takes the root mean square of forecasting error as the evaluation criterion of the network to determine the smoothing factor and uses the method of rolling forecasting to forecast the traffic flow. Compared with the forecasting models of RBF and BP neural network, GRNN has stronger approximation capability and higher forecasting accuracy.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	3816
Ending Page	3820
File Size	235693
Page Count	5
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561614
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Smoothing methods Traffic flow Neural networks Predictive models Cross validation GRNN Short-term forecasting Mathematical model Forecasting Modeling
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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