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A new approach to the prediction of passenger flow in a transit system

机译:一种预测公交系统中客流的新方法

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摘要

A non-linear model is proposed for predicting the rate of passenger flow in a transit system, and its chaotic characteristic is observed. Using wavelets analysis, the passenger flow data for a whole day are decomposed in a multi-scale way to obtain decomposition sequences. Subsequently, a neural network approach is used to predict the sequences. Finally the passenger flow value can be predicted when the predicted sequences are reconstructed. Results show that the present approach is a feasible method for passenger flow prediction.
机译:提出了一种非线性模型来预测公交系统中的客流速率,并观察了其混沌特性。使用小波分析,可以以多尺度的方式分解一整天的客流数据,以获得分解序列。随后,使用神经网络方法来预测序列。最终,当重构预测序列时,可以预测客流值。结果表明,该方法是一种可行的客流预测方法。

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