基于双层K近邻算法航站楼短时客流量预测

航站楼离港客流量在短时期内呈现准周期性规律变化，易受航班计划、天气等多种因素影响，表现出复杂的非线性特点。为了实现航站楼短时客流量的准确预测，在传统K近邻（KNN）算法基础上增加了航班计划状态模式匹配方法，以航班计划包含的多维属性作为特征选取相似历史运营日作为预测基准向量，建立基于航站楼短时客流量预测的双层K近邻模型。通过实例分析，与ARIMA模型和传统K近邻模型等进行比较，证明双层K近邻模型预测误差更小，精度更高，模型拟合度相对传统K近邻模型提高了8%~10%，为航站楼短时客流量精确预测提供了一种新的解决思路。 

Terminal building short-term passenger flow forecast based on two-tier K-nearest neighbor algorithm

Outbound passenger flow of terminal building shows the quasi-periodic variation in a short period of time and also shows complex nonlinear characteristics because of many factors such as flight schedule and weather. In order to accurately predict the short-term passenger flow of terminal building, the flight schedule state pattern matching procedure is added on the basis of the traditional K-nearest neighbor (KNN) algorithm. The flight schedule including multi-dimensional attributes is taken as a feature to select historical similar operation days as forecast reference vectors. The two-tier K-nearest neighbor model based on terminal building short-term passenger flow forecast is built. Through instance analysis and comparison with ARIMA model and traditional K-nearest neighbor model, it is proved that two-tier K-nearest neighbor model has smaller prediction error and higher precision, and the model fitting degree increases by 8%-10% compared with traditional K-nearest neighbor model. Thus the model provides a new solution for accurately forecasting terminal building short-term passenger flow.