Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow

机译：应用集合经验模式分解发现地铁站客流的时变

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摘要

This paper applies both Empirical Mode Decomposition (EmD) and Ensemble Empirical Mode Decomposition (EEMD) to extract the EMD and EEMD components from a data set of passenger flows of a station in the metro system, and illustrates the time variants of short-term passenger flow for this data sets. The results indicate that the extracted meaningful EEMD components reveal a more unique pattern than the extracted meaningful EMD components. The patterns of these EEMD components of passenger flow in the metro system are more specific and can be explained more easily for management purposes.

机译：本文应用经验模态分解（EmD）和集合经验模态分解（EEMD）来从地铁系统中一个车站的客流数据集中提取EMD和EEMD分量，并说明了短期乘客的时间变化该数据集的流程。结果表明，提取的有意义的EEMD成分比提取的有意义的EMD成分显示出更多独特的模式。地铁系统中客流的这些EEMD组件的模式更加具体，可以出于管理目的更容易解释。

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来源
《2017 4th International Conference on Industrial Engineering and Applications》|2017年|239-243|共5页
会议地点 Nagoya(JP)
作者
Mu-Chen Chen; Long-Sheng Chen; Yu Wei;
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作者单位

Department of Transportation and Logistics Management, National Chiao Tung, University, Taipei, Taiwan, R.O.C.;

Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan, R.O.C.;

Department of Railways and Highways Ministry of Transportation and Communications, Taipei, Taiwan, R.O.C.;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Correlation; Correlation coefficient; White noise; Empirical mode decomposition; Transportation; Time series analysis; Time-frequency analysis;

机译：相关;相关系数;白噪声;经验模态分解;传输;时间序列分析;时频分析;

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专利

1. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks [J] . Yu Wei, Mu-Chen Chen Transportation research . 2012,第1期

机译：基于经验模态分解和神经网络的地铁短期客流预测
2. Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling [J] . Mokhtari M. A., Sabzehparvar M. Proceedings of the Institution of Mechanical Engineers . 2019,第5期

机译：应用集成经验模式分解和扩展多点建模识别自旋气动非线性模型。
3. Real-Time Passenger Flow Anomaly Detection Considering Typical Time Series Clustered Characteristics at Metro Stations [J] . Journal of Transportation Engineering . 2020,第4期

机译：考虑地铁站典型时间序列聚类特征的实时客流异常检测
4. Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow [C] . Mu-Chen Chen, Long-Sheng Chen, Yu Wei International Conference on Industrial Engineering and Applications . 2017

机译：应用集合经验模式分解，以发现地铁站客运的时间变体
5. Application of linear genetic programming in conjunction with Kohonen's self-organizing map and ensemble empirical mode decomposition for improved streamflow prediction at lock and dam 10 along the Kentucky River approved [D] . Barge, Jonathan T. 2015

机译：将线性遗传规划与Kohonen的自组织图和整体经验模式分解相结合的应用，以改进沿肯塔基河的闸门和大坝10的水流预测
6. Correction: Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow [O] . Quanchao Chen, Di Wen, Xuqiang Li, 2020

机译：校正：基于经验模式分解的短期短期内存神经网络预测模型的短期地铁乘客流量
7. Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow [O] . Quanchao Chen, Di Wen, Xuqiang Li, 2019

机译：基于经验模式分解的短期内记忆神经网络预测模型短期地铁乘客流量

Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow

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