Predictive Intelligence: A Neural Network Learning System for Traffic Condition Prediction and Monitoring on Freeways

DOI
  • ABDUL JABBAR Rusul
    Department of Civil and Construction Engineering, Swinburne University of Technology
  • DIA Hussein
    Department of Civil and Construction Engineering, Swinburne University of Technology

抄録

<p>This research aims to develop an innovative artificial intelligence approach based on neural networks to estimate future traffic conditions on freeways. The traffic data was generated from a traffic simulation model for a busy freeway in Melbourne. Then, a predictive model was developed that estimates future speed, flow and density at forecast time horizons of 15, 30, 45 and 60 minutes into the future. The results showed a prediction rate ranging from 85% (flow) through 95% (speed) to 97% for density. This provides traffic operators with a model to predict the state of congestion on a freeway with a high degree of accuracy. Current and future work is focused on enhancing the neural network performance by training the models on larger numbers of observations obtained from the simulation model under different traffic conditions. The models will then be validated on real-time data obtained from field conditions.</p>

収録刊行物

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詳細情報 詳細情報について

  • CRID
    1390565134825358848
  • NII論文ID
    130007794455
  • DOI
    10.11175/easts.13.1785
  • ISSN
    18811124
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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