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

doi: 10.19818/j.cnki.1671-1637.2020.04.016

  • School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Natural Science Foundation of China 71771191

Sichuan Provincial Science and Technology Planning Project 2019JDRC0023

More Information
  • Author Bio:

    ZHAO Yang-yang(1991-), male, doctoral student, zyfocus@outlook.com

    JIANG Xin-guo(1975-), male, professor, PhD, xjiang@swjtu.edu.cn

  • Received Date: 2019-03-06
  • Publish Date: 2020-04-25
    Abstract
  • To weaken the interference of sample noise to the prediction model of passenger flow, a short-term metro passenger flow prediction model was proposed based on the deep-learning theory, empirical mode decomposition(EMD) and long short-term memory neural network(LSTMNN). The prediction process was divided into three stages.In the first stage, the raw automatic fare collection(AFC) data were preprocessed, and the tap-in(tap-out) passenger flow time series were constructed and decomposed into a series of intrinsic mode functions(IMFs) and a residues by the EMD. In the second stage, the input variables of the LSTMNN were determined by the partial autocorrelation function(PACF). In the third stage, the LSTMNN was developed, trained and predicted through the deep learning library Keras. A case study of Shanghai People's Square Station on metro line 2 was conducted to validate the model performance. Calculation result shows that, compared to the representative prediction models(differential autoregressive integrated moving average model, support vector machine, empirical mode decomposition and back propagation neural network, and LSTMNN), the EMD-LSTM prediction model increases the weekdays' tap-in(tap-out) passenger flow prediction accuracy of peak hour, off-peak hour, and full-day by at least 2.1%(2.5%), 2.7%(3.5%), and 2.7%(3.4%), respectively, and also increases the weekends' tap-in(tap-out) passenger flow prediction accuracy of full-day by at least 3.3%(3.5%). Thus, the EMD-LSTM is effective to predict the short-term metro passenger flow. When the forecasting step gradually increases from 5 minutes to 30 minutes, the weekdays' tap-in(tap-out)average absolute percentage prediction errors of peak hour, off-peak hour, and full-day gradually decreases from 14.8%(13.9%), 16.8%(17.4%), and 16.6%(17.0%) to 7.0%(6.2%), 8.3%(7.5%), and 8.1%(7.4%), respectively. Therefore, the forecasting error of EMD-LSTM is negatively correlated with the forecasting step length.

     

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    • References(30)
  • [1]
    CAI Chang-jun, YAO En-jian, WANG Mei-ying, et al. Prediction of urban railway station's entrance and exit passenger flow based on multiply ARIMA model[J]. Journal of Beijing Jiaotong University, 2014, 38(2): 135-140. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT201402024.htm
    [2]
    YUAN Jian, WANG Peng, WANG Yue, et al. A passenger volume prediction method based on temporal and spatial characteristics for urban rail transit[J]. Journal of Beijing Jiaotong University, 2017, 41(6): 42-48. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT201706009.htm
    [3]
    YANG Jun, HOU Zhong-sheng. A grey Markov based on large passenger flow real-time prediction model[J]. Journal of Beijing Jiaotong University, 2013, 37(2): 119-123, 128. (in Chinese). doi: 10.3969/j.issn.1673-0291.2013.02.022
    [4]
    WANG Xing-chuan, YAO En-jian, LIU Sha-sha. Urban rail transit passenger flow forecasting for large special event based on AFC data[J]. Journal of Beijing Jiaotong University, 2018, 42(1): 87-93. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT201801013.htm
    [5]
    YAO En-jian, ZHOU Wen-hua, ZHANG Yong-sheng. Real-time forecast of entrance and exit passenger flow for newly opened of urban rail transit at initial stage[J]. China Railway Science, 2018, 39(2): 119-127. (in Chinese). doi: 10.3969/j.issn.1001-4632.2018.02.15
    [6]
    LI Yang, WANG Xu-dong, SUN Shuo, et al. Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks[J]. Transportation Research Part C: Emerging Technologies, 2017, 77: 306-328. doi: 10.1016/j.trc.2017.02.005
    [7]
    SUN Yu-xing, LENG Biao, GUAN Wei. A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system[J]. Neurocomputing, 2015, 166: 109-121. doi: 10.1016/j.neucom.2015.03.085
    [8]
    LI De-wei, YAN Yi-xing, ZENG Xian-feng. Combined short-term prediction model of station entry flow in urban rail transit[J]. Urban Rapid Rail Transit, 2017, 30(1): 54-58, 64. (in Chinese). doi: 10.3969/j.issn.1672-6073.2017.01.012
    [9]
    XIONG Jie, GUAN Wei, SUN Yu-xing. Metro transfer passenger forecasting based on Kalman filter[J]. Journal of Beijing Jiaotong University, 2013, 37(3): 112-116, 121. (in Chinese). doi: 10.3969/j.issn.1673-0291.2013.03.021
    [10]
    LI Chun-xiao, LI Hai-ying, JIANG Xi, et al. Short-term entrance passenger flow forecast at urban rail transit station based on generalized dynamic fuzzy neural networks[J]. Urban Rapid Rail Transit, 2015, 28(4): 57-61. (in Chinese). doi: 10.3969/j.issn.1672-6073.2015.04.012
    [11]
    DING Chuan, WANG Dong-gen, MA Xiao-lei, et al. Predicting short-term subway ridership and prioritizing its influential factors using gradient boosting decision trees[J]. Sustainability, 2016, 8(11): 1-16.
    [12]
    LENG Biao, ZENG Jia-bei, XIONG Zhang, et al. Probability tree based passenger flow prediction and its application to the Beijing subway system[J]. Frontiers of Computer Science, 2013, 7(2): 195-203. doi: 10.1007/s11704-013-2057-y
    [13]
    ZHAO Zheng, CHEN Wei-hai, WU Xing-ming, et al. LSTM network: a deep learning approach for short-term traffic forecast[J]. IET Intelligent Transport Systems, 2017, 11(2): 68-75. doi: 10.1049/iet-its.2016.0208
    [14]
    MA Xiao-lei, TAO Zhi-min, WANG Yin-hai, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J]. Transportation Research Part C: Emerging Technologies, 2015, 54: 187-197.
    [15]
    POLSON N G, SOKOLOV V O. Deep learning for short-term traffic flow prediction[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 1-17.
    [16]
    LYU Yi-Sheng, DUAN Yan-Jie, KANG Wen-wen, et al. Traffic flow prediction with big data: a deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 865-873.
    [17]
    LIU Li-juan, CHEN Rung-ching. A novel passenger flow prediction model using deep learning methods[J]. Transportation Research Part C: Emerging Technologies, 2017, 84: 74-91.
    [18]
    HUANG Wen-hao, SONG Guo-jie, HONG Hai-kun, et al. Deep architecture for traffic flow prediction: deep belief networks with multitask learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 2191-2201.
    [19]
    HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998, 454: 903-995.
    [20]
    WEI Yu, CHEN Mu-chen. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks[J]. Transportation Research Part C: Emerging Technologies, 2012, 21(1): 148-162.
    [21]
    CHEN Syuan-yi, CHOU Wei-yao. Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach[C]∥IEEE. 15th International IEEE Conference on Intelligent Transportation Systems. New York: IEEE, 2012: 1821-1826.
    [22]
    WANG Hai-zhong, LIU Lu, DONG Shang-jia, et al. A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD-ARIMA framework[J]. Transportmetrica B: Transport Dynamics, 2016, 4(3): 159-186.
    [23]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
    [24]
    ZHONG Chen, BATTY M, MANLEY E D, et al. Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data[J]. PloS One, 2016, 11(2): 1-17.
    [25]
    TANG Li-yang, ZHAO Yang, JAVIER C, et al. Forecasting short-term passenger flow: an empirical study on Shenzhen metro[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3613-3622.
    [26]
    AN Ning, ZHAO Wei-gang, WANG Jian-zhou, et al. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting[J]. Energy, 2013, 49: 279-288.
    [27]
    ZHENG Hui-ting, YUAN Jia-bin, CHEN Long. Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation[J]. Energies, 2017, 10(8): 1-20.
    [28]
    ZHANG Xi-ke, ZHANG Qiu-wen, ZHANG Gui, et al. 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]. International Journal of Environmental Research and Public Health, 2018, 15(5): 1-23.
    [29]
    CHERKSSKY V, MA Y Q. Practical selection of SVM parameters and noise estimation for SVM regression[J]. Neural Networks, 2004, 17(1): 113-126.
    [30]
    ZHANG Wan-di, CHEN Feng, WANG Zi-jia, et al. Similarity measurement of metro travel rules based on multi-time granularities[J]. Journal of the China Railway Society, 2018, 40(4): 9-17. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201804002.htm
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