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    Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design

    Access Status
    Fulltext not available
    Authors
    Chan, Kit Yan
    Khadem, Saghar
    Dillon, Tharam
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Chan, Kit Yan and Khadem, Saghar and Dillon, Tharam. 2012. Optimization of neural network configurations for short-term traffic flow forecasting using orthogonal design, in IEEE Congress on Evolutionary Computation, Jun 10-15 2012, pp. 3002-3008. Brisbane, Qld: IEEE.
    Source Title
    Proceedings of the IEEE Congress on Evolutionary Computation
    Source Conference
    IEEE Congress on Evolutionary Computation
    DOI
    10.1109/CEC.2012.6252933
    URI
    http://hdl.handle.net/20.500.11937/3270
    Collection
    • Curtin Research Publications
    Abstract

    Neural networks have been applied for short-term traffic flow forecasting with reasonable accuracy. Past traffic flow data, which has been captured by on-road sensors, is used as the inputs of neural networks. The size of this data significantly affects the performance of short-term traffic flow forecasting, as too many inputs result in over-specification of neural networks and too few inputs result in under-learning of neural networks. However, the amount of past traffic flow data input, is usually determined by the trial and error method. In this paper, an experimental design method, namely orthogonal design, is usedto determine appropriate amount of past traffic flow data for neural networks for short-term traffic flow forecasting. The effectiveness of the orthogonal design is demonstrated by developing neural networks for short-term traffic flow forecasting based on past traffic flow data captured by on-road sensors located on a freeway in Western Australia.

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