CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

Lu Zeng (), Zinuo Li, Jie Yang and Xinyue Xu
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Lu Zeng: School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Zinuo Li: School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Jie Yang: School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Xinyue Xu: State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

IJERPH, 2022, vol. 19, issue 24, 1-19

Abstract: Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R 2 , respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Keywords: urban rail transit; short-term passenger flow prediction; complete ensemble empirical mode decomposition with adaptive noise; long-short term memory neural network; improved particle swarm optimization; combination model; CEEMDAN-IPSO-LSTM (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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