基于网络搜索信息的多模态数据驱动航空客流集成预测  

Multi-modal Data-driven Air Passenger Flow Integrated ForecastingBased on Internet Search Information

作　　者：孙景云 于婷 何林芸 SUN Jingyun;YU Ting;HE Linyun(School of Statistics,Lanzhou University of Finance and Economics,Lanzhou 730020,China;Center for Quantitative Analysis of Gansu Economic Development,Lanzhou 730020,China)

机构地区：[1]兰州财经大学统计学院,甘肃兰州730020 [2]甘肃经济发展数量分析研究中心,甘肃兰州730020

出　　处：《运筹与管理》2023年第3期155-162,共8页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(72061020,71961013);兰州财经大学2020年度高等教育教学改革研究重点项目(LJZ202008)。

摘　　要：为了对机场旅客吞吐量进行更高精度的预测,提出了一种基于网络搜索信息的“分解-重构-集成”组合预测新方法。首先,采用平均影响值和时差相关分析法对机场旅客吞吐量相关的网络搜索关键词进行筛选,合成综合搜索指数。其次,利用改进的自适应白噪声完备集合经验模态分解(ICEEMDAN)方法分别将机场旅客吞吐量和综合搜索指数分解为若干子模态序列,依据子序列的样本熵值重构为高、中、低频序列。以搜索指数中的不同频率成分作为辅助输入信息,分别对机场旅客吞吐量的高频和中频序列采用麻雀搜索算法优化的BP神经网络(SSA-BP)模型进行预测,而低频序列采用自回归分布滞后模型进行预测,最后将不同频率序列预测值用SSA-BP模型进行综合集成得到最终的预测值。通过实证发现,该组合预测新方法能显著提高预测的精度,并表现出较好的鲁棒性。Airport passenger throughput mainly refers to the number of passengers carried by flights arriving and departing.On the one hand,the airport passenger throughput can directly reflect the size and passenger capacity of the airport.On the other hand,it can reflect the degree of social and economic development of the city and its surrounding areas.However,the internal resources of the airport are relatively limited.Check-in,baggage check tracking,safety checks,waiting point scheduling and emergency response strategies all depend on the space-time distribution of the unanticipated passenger flow.By predicting the airport passenger flow timely and accurately,the management can take a precaution measures,dispatch and arrange the airport resources effectively and reasonably.This way,the airport can save operating costs,reduce the waiting time of passengers queuing,and improve passenger satisfaction.In order to predict the airport passenger throughput with higher accuracy,this paper proposes anew“decomposition-reconstruction-integration”combined forecasting method based on internet search information.First,the mean impact value and time difference correlation analysis methods are employed to screen the internet keywords related to the airport passenger throughput,and the correlation between the search volume of each keyword and the original air passenger flow data is investigated to determine the best lag period,and then a comprehensive search index is constructed.Secondly,the ICEEMDAN data decomposition method is used to decompose the airport passenger throughput and comprehensive search index into several sub-modal sequences,which are reconstructed into high,medium and low frequency sequences according to the sample entropy value of the sub-sequences.Taking the different frequency components of the search index as auxiliary input information,we predict the high frequency and medium frequency sequences of the airport passenger throughput by using the BP neural network model optimized by the sparrow search algorithm(

关 键 词：机场旅客吞吐量预测 网络搜索信息 麻雀搜索算法 ICEEMDAN分解 

分 类 号：U652[交通运输工程—港口、海岸及近海工程] TP391[交通运输工程—船舶与海洋工程]