A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

查看全文 作　　者：Dawen [1]XIA;Jian [1]GENG;Ruixi [1]HUANG;Bingqi [1]SHEN;Yang [2]HU;Yantao [3]LI;Huaqing [4]LI 机构地区：[1]College of Data Science and Information Engineering,Guizhou Minzu University,Guiyang 550025,China;[2]Department of Automotive Engineering,Guizhou Traffic Technician and Transportation College,Guiyang 550008,China;[3]College of Computer Science,Chongqing University,Chongqing 400044,China;[4]College of Electronic and Information Engineering,Southwest University,Chongqing 400715,China 出　　处：《Frontiers of Information Technology & Electronic Engineering》索引2023年第24卷第9期,共16页 基　　金：Project supported by the National Natural Science Foundation of China(Nos.62162012,62173278,and 62072061);the Science and Technology Support Program of Guizhou Province,China(No.QKHZC2021YB531);the Natural Science Research Project of Department of Education of Guizhou Province,China(Nos.QJJ2022015 and QJJ2022047);the Science and Technology Foundation of Guizhou Province,China(Nos.QKHJCZK2022YB195,QKHJCZK2022YB197,and QKHJCZK2023YB143);the Scientific Research Platform Project of Guizhou Minzu University,China(No.GZMUSYS202104);the 7^(th) Batch High-Level Innovative Talent Project of Guizhou Province,China。 摘　　要：To address the imbalance problem between supply and demand for taxis and passengers,this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit(EEMDN-SABiGRU)model on Spark for accurate passenger hotspot prediction.It focuses on reducing blind cruising costs,improving carrying efficiency,and maximizing incomes.Specifically,the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences,while dealing with the eigenmodal EMD.Next,a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid,taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid.Furthermore,the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information,to improve the accuracy of feature extraction.Finally,the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework.The experimental results demonstrate that based on the four datasets in the 00-grid,compared with LSTM,EMDLSTM,EEMD-LSTM,GRU,EMD-GRU,EEMD-GRU,EMDN-GRU,CNN,and BP,the mean absolute percentage error,mean absolute error,root mean square error,and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%,44.91%,55.04%,and 39.33%,respectively. 关 键 词：Passenger hotspot prediction Ensemble empirical mode decomposition(EEMD) Spatial attention mechanism Bi-directional gated recurrent unit(BiGRU) GPS trajectory SPARK