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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2004  >  4(4): 68-71
SHI Qi-xin, ZHENG Wei-zhong. Short-term traffic flow prediction methods comparison of road networks[J]. Journal of Traffic and Transportation Engineering, 2004, 4(4): 68-71.
Citation: SHI Qi-xin, ZHENG Wei-zhong. Short-term traffic flow prediction methods comparison of road networks[J]. Journal of Traffic and Transportation Engineering, 2004, 4(4): 68-71.
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Short-term traffic flow prediction methods comparison of road networks

  • Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China

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  • Author Bio:

    SHI Qi-xin(1946-), male, professor, 86-10-62784496, dcisqx@tsinghua.edu.cn

  • Received Date: 2004-06-07
  • Publish Date: 2004-12-25
    Abstract
  • A large number of techniques have been applied into short-term traffic flow prediction, which can be classified into two groups: statistical models and artificial neural network model. The models and their application were discussed and compared. Several models, including historical average, ARIMA (auto regressive integrated moving average) model, nonparametric regression, RBF (radial basis function) neural network and Bayesian combined neural network model were applied into a numerical example of short-term traffic volume prediction in a field network, their prediction results and performances were compared. It was found that the error of hybrid neural network model is littlest, its prediction reliability is highest, it is the most effective method to predicte short-term traffic flow.

     

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