Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

Yu Wei; Mu-Chen Chen

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Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

机译：基于经验模态分解和神经网络的地铁短期客流预测

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Short-term passenger flow forecasting is a vital component of transportation systems. The forecasting results can be applied to support transportation system management such as operation planning, and station passenger crowd regulation planning. In this paper, a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems. There are three stages in the EMD-BPN forecasting approach. The first stage (EMD Stage) decomposes the short-term passenger flow series data into a number of intrinsic mode function (IMF) components. The second stage (Com ponent Identification Stage) identifies the meaningful IMFs as inputs for BPN. The third stage (BPN Stage) applies BPN to perform the passenger flow forecasting. The historical passenger flow data, the extracted EMD components and temporal factors (i.e., the day of the week, the time period of the day, and weekday or weekend) are taken as inputs in the third stage. The experimental results indicate that the proposed hybrid EMD-BPN approach performs well and stably in forecasting the short-term metro passenger flow.

机译：短期客流预测是运输系统的重要组成部分。预测结果可用于支持运输系统管理，例如运营计划和车站乘客人数管制计划。本文提出了一种结合经验模态分解（EMD）和反向传播神经网络（BPN）的混合EMD-BPN预测方法来预测地铁系统中的短期客流。 EMD-BPN预测方法分为三个阶段。第一阶段（EMD阶段）将短期客流序列数据分解为许多固有模式函数（IMF）组件。第二阶段（组件识别阶段）将有意义的IMF识别为BPN的输入。第三阶段（BPN阶段）应用BPN进行客流预测。在第三阶段中，将历史乘客流量数据，提取的EMD分量和时间因素（即星期几，一天中的时间段以及工作日或周末）作为输入。实验结果表明，所提出的混合EMD-BPN方法在预测地铁短期客流方面表现良好且稳定。

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来源
《Transportation research》 |2012年第1期|p.148-162|共15页
作者
Yu Wei; Mu-Chen Chen;
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作者单位

Institute of Traffic and Transportation, National Chiao Tung University, 4F, No. 118, Section 1, Chung-Hsiao W. Road, Taipei 100, Taiwan, ROC;

Institute of Traffic and Transportation, National Chiao Tung University, 4F, No. 118, Section 1, Chung-Hsiao W. Road, Taipei 100, Taiwan, ROC;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
forecasting; short-term metro passenger flow; empirical mode decomposition; neural networks;

机译：预测;短期地铁乘客流量;经验模式分解神经网络;

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1. Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network [J] . Lijuan Liu, Rung-Ching Chen, Shunzhi Zhu Applied Sciences . 2020,第8期

机译：天气对使用深层LSTM神经网络的短期地铁客流预测的影响
2. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition [J] . Xike Zhang, Qiuwen Zhang, Gui Zhang, International Journal of Environmental Research and Public Health . 2018,第5期

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3. Short-term PV power forecasting using empirical mode decomposition in integration with back-propagation neural network [J] . Yadav Harendra Kumar, Pal Yash, Tripathi Madan Mohan Journal of information and optimization sciences . 2020,第1期

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4. Short-term Urban Rail Transit Passenger Flow Forecasting Based on Empirical Mode Decomposition and LSTM [C] . Zi-ji-an WANG, Chao CHEN, Xiao-le LI, International Conference on Modeling, Analysis, Simulation, Technologies and Applications . 2019

机译：基于经验模式分解和LSTM的短期城市轨道交通乘客流量预测
5. Short-term streamflow forecasting using artificial neural networks. [D] . Zealand, Cameron Michael. 1997

机译：使用人工神经网络的短期流量预测。
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

Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

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