Chaos characteristics of peak elevator traffic flow

In this paper, nonlinear time series techniques are applied to analyze the peak elevator traffic flow data. The phase space, which describes the evolution of the behaviour of a nonlinear system, is reconstructed using the delay embedding theorem suggested by Takens. The embedding parameters, e.g. the delay time and the embedding dimension are estimated using the mutual information of the data and the false nearest neighbor algorithm, respectively. Numerically the attractor of the elevator traffic flow from reconstruction is not necessarily sufficient indication of chaos, therefore we then calculate the correlation dimension of the resulting attractor and the largest Lyapunov exponent. It is demonstrated that the traffic flows of the up-peak and down-peak all exhibit low-dimensional chaotic behaviour. The result will help to adjust the group control scheduling methods according to the chaotic behaviour of the peak flow so as to increase the performance index.