吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (04): 854-860.doi: 10.7964/jdxbgxb201304002

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Bi-level model of multi-step forecasting for short-term data of loop in Sydney coordinated adaptive traffic system

LI Qi1,2, JIANG Gui-yan3   

  1. 1. College of Transportation, Jilin University, Changchun 130022, China;
    2. Qingdao Urban Planning and Design Research Institute, Qingdao 266071, China;
    3. School of Maritime and Transportation, Ningbo University, Ningbo 315211,China
  • Received:2012-05-22 Online:2013-07-01 Published:2013-07-01

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Abstract:

In order to improve the effect of multi-step forecasting for short term traffic data collected from the loop in Sydney Coordinated Adaptive Traffic System (SCATS), on the basis of data preprocessing, a bi-level model of multi-step forecasting using dynamic neural networks was designed. This model includes a multi-step forecasting method based on Nonlinear Autoregressive model with exogenous inputs (NARX) neural network and a predictable steps online estimation method based on Focused Time-Delay (FTD) neural network. Validation and comparative analysis were carried out using data of loop in SCATS measured from a megacity. The results indicate that the proposed method can further reduce the errors of short-term traffic multi-step forecasting in SCATS.

Key words: engineering of communications and transportation system, Sydney coordinated adaptive traffic system (SCATS), short-term traffic forecasts, dynamic neural network, multistep forecasts

CLC Number: 

  • U491

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