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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(4): 194-204
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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