Modeling and forecasting of urban logistics demand based on wavelet neural network

Because logistics system was an uncertain, nonlinear, dynamic and complicated system, it was difficult to describe it by traditional methods. The wavelet neural network (WNN) has the advantages of both wavelet analysis and neural network, in this paper, a modeling and forecasting method of urban logistics demand based on WNN is presented. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the effect factor of urban logistics demand. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the modeling and forecasting method can truly forecast the logistics demand by learning the index information of affect logistics demand. The actual forecasting results show that this method is feasible and effective.