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RAIL TRANSIT PASSENGER FLOW DEMAND PREDICTION METHOD AND APPARATUS BASED ON DEEP LEARNING
RAIL TRANSIT PASSENGER FLOW DEMAND PREDICTION METHOD AND APPARATUS BASED ON DEEP LEARNING
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机译：基于深度学习的轨道交通乘客流量需求预测方法和装置
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摘要
Provided are a rail transit passenger flow demand prediction method and apparatus based on deep learning. The prediction method comprises: collecting OD data, and converting the data into periodic OD two-dimensional graph sequence data; inputting the periodic OD two-dimensional graph sequence data into a spatial complex associated convolutional residual network model to output spatial feature data; inputting the spatial feature data into a time feature information extraction model to output time feature data; using the time feature data to carry out feature extraction, so as to obtain an OD passenger flow value at a prediction moment; and assessing a prediction method according to requirements. In the method, a predicted OD passenger flow value at a prediction moment is obtained by means of analyzing the multiple periodicity association of OD data and extracting feature data, and the prediction precision is high.
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