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Predictive Method for Traffic Flow of Elevator Systems Based on Neural Networks

Traffic flow prediction is an important part of elevator systems. Generally, the traffic flow of elevator systems has high complexity and randomicity and the passenger flow possesses nonlinear feature, which is difficult to be expressed by a certain functional style. In this paper, we intend to construct a predictive model of traffic flow for elevator systems using time series prediction theory based on wavelet neural network. The Morlet wavelet has been chosen in this study as the activation function. The simulation results show that the novel model has much advantages over conventional model based on linear exponential smoothing method and the novel model has such properties as simple structure of network, fast convergence and higher forecast precision.