Subway short-time passenger flow prediction method based on machine learning

The invention discloses a subway short-time passenger flow prediction method based on machine learning. On the basis of subway card shooting data, all passengers are assumed to travel according to the shortest route, and the flow of all intervals and in all stations is counted in a unit time window; subway station passenger flow in the unit time window serves as nodes, subway interval passenger flow in the unit time window serves as the weight of the edge, and a subway passenger flow network is built; features whose influences are most important to a single target interval are selected out to be brought into a follow-up regression prediction model. The recursive feature elimination algorithm is used for completing feature selection, and important features of the target interval in a target time window are selected out. The regression prediction model is built through the gradient boosted regression tree method, and subway short-time passenger flow prediction is achieved. High prediction precision can be achieved through the method under the condition that a data source is simplex. The regression prediction model is built through historical data and combined with real-time data to predict the subway short-time passenger flow, and help is provided for design optimization of urban rail transit operation marshalling.