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Open AccessArticle

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

1
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
2
Beijing Research Center of Urban Systems Engineering, Beijing 100035, China
3
Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Smart Cities 2019, 2(3), 371-387; https://doi.org/10.3390/smartcities2030023
Received: 28 May 2019 / Revised: 12 June 2019 / Accepted: 16 July 2019 / Published: 23 July 2019
(This article belongs to the Special Issue Big Data-Driven Intelligent Services in Smart Cities)
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. View Full-Text
Keywords: LSTM NN; CNN; urban rail transit; passenger flow prediction LSTM NN; CNN; urban rail transit; passenger flow prediction
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Xiong, Z.; Zheng, J.; Song, D.; Zhong, S.; Huang, Q. Passenger Flow Prediction of Urban Rail Transit Based on Deep Learning Methods. Smart Cities 2019, 2, 371-387.

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