IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i24p16433-d996723.html
   My bibliography  Save this article

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

Author

Listed:
  • Lu Zeng

    (School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
    State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
    Ganjiang Innovation Academy, Chinese Academy of Sciences, 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
    Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China)

  • Xinyue Xu

    (State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

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.

Suggested Citation

  • Lu Zeng & Zinuo Li & Jie Yang & Xinyue Xu, 2022. "CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16433-:d:996723
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/24/16433/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/24/16433/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peikun Li & Chaoqun Ma & Jing Ning & Yun Wang & Caihua Zhu, 2019. "Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    2. Pengpeng Jiao & Ruimin Li & Tuo Sun & Zenghao Hou & Amir Ibrahim, 2016. "Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, March.
    3. Wei, Ting & Chen, Shaoqing, 2020. "Dynamic energy and carbon footprints of urban transportation infrastructures: Differentiating between existing and newly-built assets," Applied Energy, Elsevier, vol. 277(C).
    4. Ruichang Mao & Yi Bao & Huabo Duan & Gang Liu, 2021. "Global urban subway development, construction material stocks, and embodied carbon emissions," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
    5. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Majerčák Peter & Majerčák Jozef & Kurenkov Petr Vladimirovič, 2023. "Impact of the COVID Crisis on Public Passenger Transport in Slovakia and Urban Transport in Žilina on a Selected Line," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 169-180, January.
    2. Mariusz Korzeń & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kunyang Chen & Guobin Zhang & Huanyu Wu & Ruichang Mao & Xiangsheng Chen, 2022. "Uncovering the Carbon Emission Intensity and Reduction Potentials of the Metro Operation Phase: A Case Study in Shenzhen Megacity," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    2. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(C).
    3. Zhang, Qi & Gu, Baihe & Zhang, Haiying & Ji, Qiang, 2023. "Emission reduction mode of China's provincial transportation sector: Based on “Energy+” carbon efficiency evaluation," Energy Policy, Elsevier, vol. 177(C).
    4. Li, Jingjing & Nian, Victor & Jiao, Jianling, 2022. "Diffusion and benefits evaluation of electric vehicles under policy interventions based on a multiagent system dynamics model," Applied Energy, Elsevier, vol. 309(C).
    5. Chen, Yu & Lin, Boqiang, 2021. "Understanding the green total factor energy efficiency gap between regional manufacturing—insight from infrastructure development," Energy, Elsevier, vol. 237(C).
    6. Charles Gillott & Will Mihkelson & Maud Lanau & Dave Cheshire & Danielle Densley Tingley, 2023. "Developing Regenerate: A circular economy engagement tool for the assessment of new and existing buildings," Journal of Industrial Ecology, Yale University, vol. 27(2), pages 423-435, April.
    7. Haihong Song & Liyuan Gu & Yifan Li & Xin Zhang & Yuan Song, 2022. "Research on Carbon Emission Efficiency Space Relations and Network Structure of the Yellow River Basin City Cluster," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    8. Yuhao Yang & Fengying Yan, 2023. "An Inquiry into the Characteristics of Carbon Emissions in Inter-Provincial Transportation in China: Aiming to Typological Strategies for Carbon Reduction in Regional Transportation," Land, MDPI, vol. 13(1), pages 1-24, December.
    9. Xiaodong Hu & Ximing Zhang & Lei Dong & Hujun Li & Zheng He & Huihua Chen, 2022. "Carbon Emission Factors Identification and Measurement Model Construction for Railway Construction Projects," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    10. Wang, Xuliang & Xu, Lulu & Ye, Qin & He, Shi & Liu, Yi, 2022. "How does services agglomeration affect the energy efficiency of the service sector? Evidence from China," Energy Economics, Elsevier, vol. 112(C).
    11. Xiaoqin Chen & Shenya Mao & Siqi Lv & Zhong Fang, 2022. "A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry," IJERPH, MDPI, vol. 19(19), pages 1-18, September.
    12. Zhang, Qian & Liu, Xiaoxiao & Spurgeon, Sarah & Yu, Dingli, 2021. "A two-layer modelling framework for predicting passenger flow on trains: A case study of London underground trains," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 119-139.
    13. Zhiqiang Zhu & Xuechi Zhang & Mengqing Xue & Yaoyao Song, 2023. "Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    14. Ting Chen & Jianxiao Ma & Shuang Li & Zhenjun Zhu & Xiucheng Guo, 2023. "Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    15. Pengbang Wei & Yufang Peng & Weidong Chen, 2022. "Climate change adaptation mechanisms and strategies of coal-fired power plants," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(8), pages 1-22, December.
    16. Xianwang Li & Zhongxiang Huang & Saihu Liu & Jinxin Wu & Yuxiang Zhang, 2023. "Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)," Sustainability, MDPI, vol. 15(10), pages 1-30, May.
    17. Lin, Boqiang & Xu, Bin, 2020. "Effective ways to reduce CO2 emissions from China's heavy industry? Evidence from semiparametric regression models," Energy Economics, Elsevier, vol. 92(C).
    18. Song, Yao-yao & Li, Jing-jing & Wang, Jin-li & Yang, Guo-liang & Chen, Zhenling, 2022. "Eco-efficiency of Chinese transportation industry: A DEA approach with non-discretionary input," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    19. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    20. Liu, Yang & Dong, Kangyin & Wang, Jianda & Taghizadeh-Hesary, Farhad, 2023. "Towards sustainable development goals: Does common prosperity contradict carbon reduction?," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 70-88.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16433-:d:996723. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.