基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测

夏大文; 耿建; 黄瑞曦; 申冰琪; 胡杨; 李艳涛; 李华青

doi:10.1631/FITEE.2200621

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基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测

常规文章 | Updated：2023-09-21

- 基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测
  Enhanced Publication
- A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction
- 信息与电子工程前沿（英文） 2023年24卷第9期页码：1316-1331
- Affiliations：
  
  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
- Author bio：
  
  ‡Corresponding authors
- Funds：
  
  National Natural Science Foundation of China(62162012;62173278;62072061);Science and Technology Support Program of Guizhou Province, China(QKHZC2021YB531);Natural Science Research Project of Department of Education of Guizhou Province, China(QJJ2022015;QJJ2022047);Science and Technology Foundation of Guizhou Province, China(QKHJCZK2022YB195;QKHJCZK2022YB197;QKHJCZK2023YB143);Scientific Research Platform Project of Guizhou Minzu University, China(GZMUSYS202104);7th Batch High-Level Innovative Talent Project of Guizhou Province, China
- DOI：10.1631/FITEE.2200621
  中图分类号： TP39
- 纸质出版日期：2023-09-0 ，
  
  收稿日期：2022-12-05，
  
  录用日期：2023-04-11
- Accepted：
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夏大文, 耿建, 黄瑞曦, 等. 基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测[J]. 信息与电子工程前沿（英文）, 2023,24(9):1316-1331.

DAWEN XIA, JIAN GENG, RUIXI HUANG, et al. A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction. [J]. Frontiers of information technology & electronic engineering, 2023, 24(9): 1316-1331.
夏大文, 耿建, 黄瑞曦, 等. 基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测[J]. 信息与电子工程前沿（英文）, 2023,24(9):1316-1331. DOI： 10.1631/FITEE.2200621.

DAWEN XIA, JIAN GENG, RUIXI HUANG, et al. A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction. [J]. Frontiers of information technology & electronic engineering, 2023, 24(9): 1316-1331. DOI： 10.1631/FITEE.2200621.

摘要

针对出租车与乘客之间的供需不平衡问题，本文提出一种基于Spark的分布式归一化集合经验模态分解和面向空间注意力机制的双向门控循环单元（EEMDN-SABiGRU）模型，实现乘客热点的精准预测，旨在于降低盲目巡航开支、提高载客效率和实现收益最大化。首先，提出一种归一化的集合经验模态分解方法（EEMDN），处理网格中乘客热点数据，解决非平稳序列问题和数值差异过大造成的预测精度下降问题，避免EMD本征模态函数（IMF）存在的模态混叠现象。其次，构建一种基于乘客上下车热点的权重和乘客的空间规律性的空间注意力机制，捕捉每个网格中的乘客热点特征。再次，融合一种双向门控循环单元（GRU）算法，解决GRU仅能获取前向信息而忽略后向信息问题，提高特征提取的准确性。最后，在Spark并行计算框架下，采用真实的出租车GPS轨迹数据，基于EEMDN-SABiGRU模型实现了乘客热点的准确预测。实验结果表明，在00网格4个数据集上，与LSTM、EMDL-STM、EEMD-LSTM、GRU、EMD-GRU、EEMD-GRU、EMDN-GRU、CNN和BP相比，EEMDN-SABiGRU的平均绝对百分比误差、平均绝对误差、均方根误差和最大误差值分别降低了43.18%、44.91%、55.04%和39.33%。

Abstract

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

EMD-LSTM

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

乘客热点预测集合经验模态分解（EEMD）空间注意力机制双向门控循环单元（BiGRU）GPS轨迹Spark

Keywords

Passenger hotspot predictionEnsemble empirical mode decomposition (EEMD)Spatial attention mechanismBi-directional gated recurrent unit (BiGRU)GPS trajectorySpark

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