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    Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

    172021_172021.pdf (1.951Mb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Dillon, Tharam
    Singh, Jaipal
    Chang, Elizabeth
    Date
    2011
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, Kit Yan and Dillon, Tharam S. and Singh, Jaipal and Chang, Elizabeth. 2012. Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm. IEEE Transactions on Intelligent Transportation Systems. 13 (2): pp. 644-654.
    Source Title
    IEEE Transactions on Intelligent Transportation Systems
    DOI
    10.1109/TITS.2011.2174051
    ISSN
    15249050
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/28800
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg–Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.

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