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基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型

赵阳阳 夏亮 江欣国

赵阳阳, 夏亮, 江欣国. 基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型[J]. 交通运输工程学报, 2020, 20(4): 194-204. doi: 10.19818/j.cnki.1671-1637.2020.04.016
引用本文: 赵阳阳, 夏亮, 江欣国. 基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型[J]. 交通运输工程学报, 2020, 20(4): 194-204. doi: 10.19818/j.cnki.1671-1637.2020.04.016
ZHAO Yang-yang, XIA Liang, JIANG Xin-guo. Short-term metro passenger flow prediction based on EMD-LSTM[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 194-204. doi: 10.19818/j.cnki.1671-1637.2020.04.016
Citation: ZHAO Yang-yang, XIA Liang, JIANG Xin-guo. Short-term metro passenger flow prediction based on EMD-LSTM[J]. Journal of Traffic and Transportation Engineering, 2020, 20(4): 194-204. doi: 10.19818/j.cnki.1671-1637.2020.04.016

基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型

doi: 10.19818/j.cnki.1671-1637.2020.04.016
基金项目: 

国家自然科学基金项目 71771191

四川省科技计划项目 2019JDRC0023

详细信息
    作者简介:

    赵阳阳(1991-), 男, 陕西延安人, 西南交通大学工学博士研究生, 从事轨道客流数据挖掘研究

    江欣国(1975-), 男, 福建闽侯人, 西南交通大学教授, 工学博士

  • 中图分类号: U293.5

Short-term metro passenger flow prediction based on EMD-LSTM

Funds: 

National Natural Science Foundation of China 71771191

Sichuan Provincial Science and Technology Planning Project 2019JDRC0023

More Information
  • 摘要: 为降低样本噪声对客流预测模型的干扰, 结合深度学习理论, 提出了一种基于经验模态分解与长短时记忆神经网络的短时地铁客流预测模型; 将预测过程分为3个阶段, 第1阶段预处理原始地铁刷卡数据, 构建进(出)站客流时间序列, 运用经验模态分解法将时间序列转化为一系列本征模函数及残差, 第2阶段利用偏自相关函数确定长短时记忆神经网络的输入变量, 第3阶段基于深度学习库Keras, 完成长短时记忆神经网络的搭建、训练及预测; 以上海地铁2号线人民广场站客流数据验证了模型的有效性。计算结果表明: 与代表性的预测模型(差分自回归移动平均模型、支持向量机、经验模态分解与反向传播神经网络、长短时记忆神经网络)相比, 经验模态分解与长短时记忆神经网络预测模型分别将工作日高峰、平峰、全日的进(出)站客流预测精度分别至少提升了2.1%(2.5%)、2.7%(3.5%)、2.7%(3.4%), 将非工作日全日的进(出)站客流预测精度至少提升了3.3%(3.5%), 说明经验模态分解与长短时记忆神经网络的组合是一种预测短时地铁客流的有效模型; 当预测步长由5 min逐渐增加至30 min时, 工作日高峰、平峰和全日进(出)站客流的平均绝对百分比预测误差分别由14.8%(13.9%)、16.8%(17.4%)和16.6%(17.0%)逐渐降低至7.0%(6.2%)、8.3%(7.5%)和8.1%(7.4%), 说明该方法预测误差与预测步长呈负相关。

     

  • 图  1  LSTMNN结构

    Figure  1.  Structure of LSTMNN

    图  2  EMD-LSTM预测流程

    Figure  2.  Prediction flow of EMD-LSTM

    图  3  日进站客流序列EMD分解结果

    Figure  3.  EMD decomposition results of daily tap-in passenger flow sequence

    图  4  本征模函数分量和残差的偏自相关系数

    Figure  4.  Partial autocorrelation coefficients of IMFs and residue

    图  5  进站客流预测结果

    Figure  5.  Prediction results of tap-in passenger flow

    图  6  出站客流预测结果

    Figure  6.  Prediction results of tap-out passenger flow

    图  7  不同步长预测误差

    Figure  7.  Prediction errors under different steps

    表  1  预测误差对比

    Table  1.   Comparison of prediction errors

    客流类型 模型 工作日 非工作日
    高峰 平峰 全日 全日
    E1/% E2 E1/% E2 E1/% E2 E1/% E2
    进站 ARIMA 15.23 695 17.17 237 16.93 308 18.43 320
    SVM 14.21 642 16.40 205 16.13 274 17.62 298
    LSTM 12.41 540 15.39 189 15.03 264 16.05 271
    EMD-BPNN 10.75 473 13.49 175 13.16 248 14.56 254
    EMD-LSTM 8.62 368 10.75 161 10.49 211 11.23 198
    出站 ARIMA 14.61 728 16.92 251 16.64 302 17.68 310
    SVM 13.29 690 16.49 220 16.10 298 17.60 308
    LSTM 11.92 659 15.54 204 15.10 291 16.39 289
    EMD-BPNN 9.76 562 13.07 164 12.67 246 14.62 261
    EMD-LSTM 7.31 421 9.56 117 9.29 175 11.08 178
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-03-06
  • 刊出日期:  2020-04-25

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