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    Development of neural network based traffic flow predictors using pre-processed data

    Access Status
    Fulltext not available
    Authors
    Chan, Kit Yan
    Yiu, Ka Fai
    Date
    2014
    Type
    Book Chapter
    
    Metadata
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    Citation
    Chan, K.Y. and Yiu, K.F. 2014. Development of neural network based traffic flow predictors using pre-processed data, in Xu, H. and Wang, X. (ed), Optimization and Control Methods in Industrial Engineering and Construction. pp. 125-138. Netherlands: Springer.
    Source Title
    Optimization and Control Methods in Industrial Engineering and Construction
    DOI
    10.1007/978-94-017-8044-5_8
    ISBN
    978-94-017-8043-8
    URI
    http://hdl.handle.net/20.500.11937/18134
    Collection
    • Curtin Research Publications
    Abstract

    Neural networks have commonly been applied for traffic flow predictions. Generally, the past traffic flow data captured by on-road detector stations, is used to train the neural networks. However, recently research mostly focuses on development of innovative neural networks, while it lacks development of mechanisms on pre-processing traffic flow data priors on training in order to obtain more accurate neural networks. In this chapter, a simple but effective training method is proposed by incorporating the mechanisms of back-propagation algorithm and the exponential smoothing method, which is proposed to pre-process traffic flow data before training purposes. The pre-processing approach intends to aid the back-propagation algorithm to develop more accurate neural networks, as the pre-processed traffic flow data is more smooth and continuous than the original unprocessed traffic flow data. This approach was evaluated based on some sets of traffic flow data captured on a section of the freeway in Western Australia. Experimental results indicate that the neural networks developed based on this pre-processed data outperform those that are developed based on either original data or data which is preprocessed by the other pre-processing approaches.

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