基于KNN回归的客运枢纽聚集人数组合预测方法

Combination forecasting model for number of assembling passengers at transportation terminal based on KNN regression algorithm

为精准预测客运枢纽聚集人数以形成合理科学的客运枢纽客流组织方案,提出了一种基于KNN回归算法的客运枢纽聚集人数组合预测方法。在分析客运枢纽客流聚集规律的基础上,以数值相似和趋势相似为原则运用KNN回归算法预测区域聚集人数,并综合考虑各自特点引入时变权重系数进行组合预测,解决了以往KNN回归预测模型所需历史数据量大和运行时间长等方面的不足。实例分析结果表明,本文方法在非节假日平均预测精度可达95%以上,在春运期间平均预测精度可达90%,均高于移动平均法、卡尔曼滤波法与灰色预测法。

To work out a reasonable and scientific passenger-flow organization scheme with an accurate assembling passenger prediction for transportation terminals,a combination forecasting model based on the KNN regression algorithm was proposed.Grounded on the analysis of the assembling laws of passengers at transportation terminals,the KNN regression algorithm was applied to forecast the number of assembling passengers based on the principles of the numerical similarity and the trend similarity.With the comprehensive consideration of the respective characteristics,the combination forecasting was realized by introducing a time-varying weight coefficient.As a result,the proposed model could solve the shortcomings of the previous KNN regression prediction model,such as large amount of historical data and long running time.The experimental results suggest that the average prediction accuracy of the proposed method can be guaranteed over 95%during non-holidays and 90%during the Spring Festival travel rush,which is superior to moving average method,Kalman filter model and gray prediction method respectively.