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    Traffic flow forecasting neural networks based on exponential smoothing method

    Access Status
    Fulltext not available
    Authors
    Chan, Kit Yan
    Singh, Jaipal
    Dillon, Tharam
    Chang, Elizabeth
    Date
    2011
    Type
    Conference Paper
    
    Metadata
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    Citation
    Chan, Kit and Singh, Jaipal and Dillon, Tharam and Chang, Elizabeth. 2011. Traffic flow forecasting neural networks based on exponential smoothing method, in 6th IEEE Conference on Industrial Electronics and Applications (ICIEA 2011), Mar 21-23 2011. Beijing, China: IEEE.
    Source Title
    Proceedings of the 6th IEEE conference on industrial electronics and applications (ICIEA 2011)
    Source Conference
    6th IEEE Conference on Industrial Electronics and Applications (ICIEA 2011)
    DOI
    10.1109/ICIEA.2011.5975612
    ISBN
    978-1-4244-8755-4
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/10727
    Collection
    • Curtin Research Publications
    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.

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    • Neural Network Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm
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      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 ...
    • Development of neural network based traffic flow predictors using pre-processed data
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      Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast ...
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