吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 399-405.doi: 10.13229/j.cnki.jdxbgxb201602010

• Orginal Article • Previous Articles     Next Articles

Short-term forecasting of parking space using particle swarm optimization-wavelet neural network model

JI Yan-jie1, 2, CHEN Xiao-shi1, 2, WANG Wei1, 2, HU Bo1, 2   

  1. 1.School of Transportation, Southeast University, Nanjing 210096, China;
    2.Jiangsu Province Collaborative Innovation Center of Modern Urben Traffic Technologies,Nanjing 210096,China
  • Received:2014-05-06 Online:2016-02-20 Published:2016-02-20

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Abstract: A forecasting model was proposed based on the short-term changing characteristics of Available Parking Space (APS). This model integrates the wavelet analysis, Particle Swarm Optimization (PSO) and Wavelet Neural Network (WNN). First, the APS time series were decomposed and reconstituted by wavelet analysis. Then, WNN model was used to forecast the reconstructed time series respectively. The PSO method was employed to optimize the selection of the initial parameters of the neural network. Finally, the final forecasted ASP was induced by integrating the prediction results. A case study was carried out to verify the applicability of the proposed model. Compared with simple WNN model, the new method enjoys higher accuracy and stable performance that the APS forecasting can be improved by 5 to 7 times by this new method.

Key words: engineering of communication and transportation system, available parking space, short-term forecasting, wavelet, particle swarm optimization, wavelet neural network

CLC Number: 

  • U491
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