The use of deep learning combined with wavelet analysis in short-term passenger flow forecast of urban rail transit

2021 ◽  
Author(s):  
haiyong yu ◽  
Lili Huang ◽  
Kang Chen
Keyword(s):  
Deep Learning ◽  
Wavelet Analysis ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Short Term ◽  
Passenger Flow ◽  
Urban Rail ◽  
Flow Forecast

scholarly journals Passenger Flow Prediction of Urban Rail Transit Based on Deep Learning Methods

Smart Cities ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 371-387 ◽  
Author(s):  
Zhi Xiong ◽  
Jianchun Zheng ◽  
Dunjiang Song ◽  
Shaobo Zhong ◽  
Quanyi Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Moving Average ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Short Term ◽  
Learning Methods ◽  
Autoregressive Integrated Moving Average ◽  
Passenger Flow ◽  
Urban Rail

The rapid development of urban rail transit brings high efficiency and convenience. At the same time, the increasing passenger flow also remarkably increases the risk of emergencies such as passenger stampedes. The accurate and real-time prediction of dynamic passenger flow is of great significance to the daily operation safety management, emergency prevention, and dispatch of urban rail transit systems. Two deep learning neural networks, a long short-term memory neural network (LSTM NN) and a convolutional neural network (CNN), were used to predict an urban rail transit passenger flow time series and spatiotemporal series, respectively. The experiments were carried out through the passenger flow of Beijing metro stations and lines, and the prediction results of the deep learning methods were compared with several traditional linear models including autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and space–time autoregressive integrated moving average (STARIMA). It was shown that the LSTM NN and CNN could better capture the time or spatiotemporal features of the urban rail transit passenger flow and obtain accurate results for the long-term and short-term prediction of passenger flow. The deep learning methods also have strong data adaptability and robustness, and they are more ideal for predicting the passenger flow of stations during peaks and the passenger flow of lines during holidays.

Short-term passenger flow forecast of urban rail transit based on GPR and KRR

2019 ◽  
Vol 13 (9) ◽  
pp. 1374-1382 ◽  
Author(s):  
Zhiqiang Guo ◽  
Xin Zhao ◽  
Yaxin Chen ◽  
Wei Wu ◽  
Jie Yang
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Short Term ◽  
Passenger Flow ◽  
Urban Rail ◽  
Flow Forecast
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