With the improvement of the national economic level, more and more large-scale activities are held in big cities. The activities lead to the concentration of people in the region, which not only causes great pressure on the surrounding infrastructure, but also gives rise to group events easily. So this article collects the sports event information and AFC date for nearly three years, analysis of the traffic characteristics around the activities. Than explore the date attribute, activity type, weather, home and away teams which as the number of reported several influence factors on the influence of induced passenger flow at the station. Based on this, a gradient boosting decision tree (GBDT) prediction model was constructed and verified by an activity. The prediction results show that the average prediction accuracy of passenger flow out of Dongdaqiao, Dongsishitiao and Tuanjiehu station in 15 minutes is 93.67%, 90.76% and 89.61%, respectively. The prediction accuracy of inbound passenger flow are 80.68%, 78.96% and 78.11%. This study can provide theoretical support for the formulation of metro emergency plan and passenger flow evacuation plan during large-scale activities. Keywords: Passenger flow prediction; Large events; Sports events; Metro station; Passenger flow characteristics Keywords: passenger flow prediction; Large events; Sports events; Orbital station; Passenger flow characteristics

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