Method for predicting short-time passenger flow of urban rail transit station during large-scale activity

The invention relates to an urban rail transit station short-time passenger flow prediction method in a large-scale activity period. The method comprises the following steps: acquiring urban rail transit in-out station passenger flow data; performing statistics on the inbound and outbound passenger flow data based on a rail transit station number and a set time interval to obtain a time-sharing passenger flow; performing onehot coding on the activity time to obtain an activity code, splicing the passenger flow data and the activity code according to the time, and performing normalization processing to obtain a historical data matrix; constructing a large-scale activity short-term passenger flow prediction model based on a convolutional neural network, an attention mechanism and a long-short-term memory neural network; and predicting the passenger flow in the target time period by using the short-time passenger flow prediction model. Redundant data are eliminated through similarity measurement, and the model p