Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising

Qingliang Zhao, Xiaobin Feng, Liwen Zhang and Yiduo Wang ()
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Qingliang Zhao: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Xiaobin Feng: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Liwen Zhang: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Yiduo Wang: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

Mathematics, 2023, vol. 11, issue 19, 1-16

Abstract: Urban rail transit offers advantages such as high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou’s Shantang Street station. Shantang Street’s high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case, making it a representative subway station. Wavelet denoising and Long Short Term Memory (LSTM) were combined to predict short-term flows, comparing the results to those of standalone LSTM, Support Vector Regression (SVR), Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average Model (ARIMA). This study illustrates that the algorithms adopted exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance. The research findings can provide fundamental recommendations for implementing appropriate passenger flow control measures at stations and offer effective references for predicting passenger flow and mitigating traffic pressure in various cities.

Keywords: rail transit short-term passenger flow prediction; LSTM; wavelet denoising analysis; SVR (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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