关键词: 混合EMD-BPNN方法, 地铁客流预测, 城市轨道交通

Abstract: Through the prediction of passenger flow of urban rail transit, the purpose of improving passenger travel efficiency and reducing operating costs can be achieved. Based on this, a hybrid EMD-BPNN method combining empirical mode decomposition and neural network is proposed to predict the short-term passenger flow. The method decomposes the original passenger flow data into multiple intrinsic modal function components through empirical mode decomposition, and filters out the meaningful components, which are used as the input of the neural network to predict the passenger flow. The experimental results showed that the method proposed in this paper is more accurate and stable than the traditional neural network algorithm in the short-term metro passenger flow prediction.

Key words: hybrid EMD-BPNN method, metro passenger flow prediction, urban rail transit