NDLI: Traffic flow forecasting neural networks based on exponential smoothing method

Content Provider	IEEE Xplore Digital Library
Author	Chan, K.Y. Dillon, T.S. Singh, J. Chang, E.
Copyright Year	2011
Description	Author affiliation: Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth, Australia (Chan, K.Y.; Dillon, T.S.; Singh, J.; Chang, E.)
Abstract	This paper discusses a neural network development approach based on an exponential smoothing method which aims at enhancing previously used neural networks for traffic flow forecasting. The approach uses the exponential smoothing method to pre-process traffic flow data before implementing on neural networks for training purpose. The pre-processed traffic flow data, which is lesser non-smooth, discontinuous and lumpy than the original traffic flow data, is more suitable to use for neural network training. This neural network development approach was evaluated by forecasting real-time traffic conditions on a section of the freeway in Western Australia. Regarding training errors which indicate capability in fitting traffic flow data, the neural network models developed by the proposed approach was capable to achieve more than 20% of the rate of improvement relative to the neural network developed based on the original traffic flow data. Regarding testing errors which indicate generalization capability for traffic flow forecasting, the neural network models developed by the proposed approach was capable in achieving more than 8% of the rate of improvement relative to the neural networks developed based on the original traffic flow data.
Starting Page	376
Ending Page	381
File Size	234175
Page Count	6
File Format	PDF
ISBN	9781424487547
e-ISBN	9781424487561
e-ISBN	9781424487554
DOI	10.1109/ICIEA.2011.5975612
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Forecasting Neural networks Training Smoothing methods Data models Predictive models Genetic algorithms exponential smoothing neural network traffic flow forecasting
Content Type	Text
Resource Type	Article

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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
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