Processing of Airport Passenger Flow Abnormal Data Based on K Nearest Neighbor

The big data accumulated during the operation of the airport has important research value. However, these data often have the characteristics of clutter, redundancy, incompleteness, and noise, and cannot be used directly. The original data needs to be pre-processed. This paper designs an interpolation algorithm based on K nearest neighbors (KNN), which is used to solve the passenger flow statistical error caused by abnormal time data of passenger check-in, and the feasibility of the method is verified by experiments. Experimental results show that this method can effectively solve the problem of abnormal passenger flow data.