NDLI: Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach

Content Provider	IEEE Xplore Digital Library
Author	Syuan-Yi Chen Wei-Yao Chou
Copyright Year	2012
Description	Author affiliation: Information and Communications Research Laboratories, Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd, Chutung, Hsinchu 31040, Taiwan (Syuan-Yi Chen; Wei-Yao Chou)
Abstract	An empirical mode decomposition based recurrent Hermite neural network (ERHNN) prediction model is proposed to predict short-term traffic flow in this study. First, a recurrent Hermite neural network (RHNN) prediction model with different orthonormal Hermite polynomial basis functions (OHPBFs) as activation functions is introduced. Then, to further mitigate the influence of noise and improve the accuracy of prediction, an empirical mode decomposition (EMD) method is derived to decompose the original short-term traffic flow data into several intrinsic mode functions (IMFs) and adopt them as the inputs for the RHNNs. Therefore, an ERHNN prediction model, which comprises good predictive ability for the nonlinear and non-stationary signals through the combination of the merits of OHPBFs, EMD and EHNN, is proposed to predict short-term traffic flow more effectively. The validity of the ERHNN prediction model is verified using all day short-term traffic flow data at high way I–80W in California. Simulation results demonstrate that the proposed ERHNN prediction model is with superior performance compared with the pure recurrent neural network (RNN) and RHNN prediction models.
Starting Page	1821
Ending Page	1826
File Size	736219
Page Count	6
File Format	PDF
ISBN	9781467330640
ISSN	21530009
e-ISBN	9781467330633
e-ISBN	9781467330626
DOI	10.1109/ITSC.2012.6338665
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Neurons Time series analysis Data models Artificial neural networks Noise Simulation
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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