PMID- 36554314
OWN - NLM
STAT- MEDLINE
DCOM- 20221226
LR  - 20230124
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 19
IP  - 24
DP  - 2022 Dec 7
TI  - CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in 
      Urban Rail Transit Systems.
LID - 10.3390/ijerph192416433 [doi]
LID - 16433
AB  - 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.
FAU - Zeng, Lu
AU  - Zeng L
AUID- ORCID: 0000-0002-3333-8978
AD  - School of Electrical Engineering, Jiangxi University of Science and Technology, 
      Ganzhou 341000, China.
AD  - State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, 
      Beijing 100044, China.
AD  - Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China.
FAU - Li, Zinuo
AU  - Li Z
AD  - School of Electrical Engineering, Jiangxi University of Science and Technology, 
      Ganzhou 341000, China.
FAU - Yang, Jie
AU  - Yang J
AD  - School of Electrical Engineering, Jiangxi University of Science and Technology, 
      Ganzhou 341000, China.
AD  - Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China.
FAU - Xu, Xinyue
AU  - Xu X
AD  - State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, 
      Beijing 100044, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221207
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - Humans
MH  - *COVID-19
MH  - Transportation/methods
MH  - Neural Networks, Computer
MH  - Public Health
MH  - *Malocclusion
PMC - PMC9779204
OTO - NOTNLM
OT  - CEEMDAN-IPSO-LSTM
OT  - combination model
OT  - complete ensemble empirical mode decomposition with adaptive noise
OT  - improved particle swarm optimization
OT  - long-short term memory neural network
OT  - short-term passenger flow prediction
OT  - urban rail transit
COIS- The authors declare no conflict of interest.
EDAT- 2022/12/24 06:00
MHDA- 2022/12/27 06:00
PMCR- 2022/12/07
CRDT- 2022/12/23 01:26
PHST- 2022/11/09 00:00 [received]
PHST- 2022/11/25 00:00 [revised]
PHST- 2022/12/02 00:00 [accepted]
PHST- 2022/12/23 01:26 [entrez]
PHST- 2022/12/24 06:00 [pubmed]
PHST- 2022/12/27 06:00 [medline]
PHST- 2022/12/07 00:00 [pmc-release]
AID - ijerph192416433 [pii]
AID - ijerph-19-16433 [pii]
AID - 10.3390/ijerph192416433 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2022 Dec 7;19(24):16433. doi: 
      10.3390/ijerph192416433.