基于支持向量机回归的机场航班延误预测

Airport flight delay prediction based on SVM regression

航班延误情况的预测对于繁忙机场意义重大。针对繁忙机场的进离港延误航班数量和延误时间难以预测的问题,采用支持向量机回归方法建立航班进离港延误预测模型。首先根据已有的航班运营数据,利用向后逐步选择算法,分别挖掘出与机场单位小时进离港延误航班数和总延误时间最为相关的因素,并将其作为预测变量来预测延误水平。其次,利用Grid-Search和交叉检验法选择最优的模型参数。最后,使用洛杉矶机场与浦东机场航班起降数据来训练模型,并将真实数据集作为输入变量分别用于多元线性回归模型和支持向量机回归模型,比较航班延误预测效果。结果表明:支持向量机回归模型能够很好预测航班延误的趋势,较为准确地预测航班延误。

Flight delay prediction is significant for busy airports. Aiming at the difficuhy of predicting the number and duration of delays in busy airport flights, SVM（support vector machine） regression method is used to establish the flight arrival/departure delay prediction model. First of all, according to the flight operating data, data mining and backward stepwise selection algorithm are used to determine the most relevant factors of number and duration of delay per hour respectively. Secondly, grid-search and cross-check methods are used to select the optimal model parameters. Finally, historical data of LAX（Los Angeles International Airport） and PVG（Pudong International Airport） are used to train the model, and multivariate linear regression model and SVM regression model are applied to test the current model. Comparison results show that the SVM regression model can achieve better prediction effect.