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    Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

    189212_189212.pdf (199.4Kb)
    Access Status
    Open access
    Authors
    Chan, Kit
    Khadem, Saghar
    Dillon, Tharam
    Palade, Vasile
    Singh, Jaipal
    Chang, Elizabeth
    Date
    2012
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, K.Y. and Khadem, S. and Dillon, Tharam S. and Palade, V. and Singh, J. and Chang, E. 2012. Selection of significant on-road sensor data for short-term traffic flow forecasting using the Taguchi method. IEEE Transactions on Industrial Informatics. 8 (2): pp. 255-266.
    Source Title
    IEEE Transactions on Industrial Informatics
    DOI
    10.1109/TII.2011.2179052
    ISSN
    15513203
    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/20.500.11937/21506
    Collection
    • Curtin Research Publications
    Abstract

    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 future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase.

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